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VTON-IT: Virtual Try-On using Image Translation (2310.04558v2)

Published 6 Oct 2023 in cs.CV and cs.LG

Abstract: Virtual Try-On (trying clothes virtually) is a promising application of the Generative Adversarial Network (GAN). However, it is an arduous task to transfer the desired clothing item onto the corresponding regions of a human body because of varying body size, pose, and occlusions like hair and overlapped clothes. In this paper, we try to produce photo-realistic translated images through semantic segmentation and a generative adversarial architecture-based image translation network. We present a novel image-based Virtual Try-On application VTON-IT that takes an RGB image, segments desired body part, and overlays target cloth over the segmented body region. Most state-of-the-art GAN-based Virtual Try-On applications produce unaligned pixelated synthesis images on real-life test images. However, our approach generates high-resolution natural images with detailed textures on such variant images.

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References (29)
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[2021] Liu, Y., Zhao, M., Zhang, Z., Zhang, H., Yan, S.: Arbitrary virtual try-on network: Characteristics preservation and trade-off between body and clothing. arXiv preprint arXiv:2111.12346 (2021) Jocher et al. [2021] Jocher, G., Stoken, A., Chaurasia, A., Borovec, J., NanoCode012, TaoXie, Kwon, Y., Michael, K., Changyu, L., Fang, J., V, A., Laughing, tkianai, yxNONG, Skalski, P., Hogan, A., Nadar, J., imyhxy, Mammana, L., AlexWang1900, Fati, C., Montes, D., Hajek, J., Diaconu, L., Minh, M.T., Marc, albinxavi, fatih, oleg, wanghaoyang0106: ultralytics/yolov5: v6.0 - YOLOv5n ’Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support. Zenodo (2021). https://doi.org/10.5281/zenodo.5563715 . https://doi.org/10.5281/zenodo.5563715 Lin et al. [2014] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., Catanzaro, B.: High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. arXiv (2017). https://doi.org/10.48550/ARXIV.1711.11585 . https://arxiv.org/abs/1711.11585 Dong et al. [2019] Dong, H., Liang, X., Shen, X., Wu, B., Chen, B.-C., Yin, J.: Fw-gan: Flow-navigated warping gan for video virtual try-on. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1161–1170 (2019) Guo et al. [2019] Guo, S., Huang, W., Zhang, X., Srikhanta, P., Cui, Y., Li, Y., Scott, M.R., Adam, H., Belongie, S.: The iMaterialist Fashion Attribute Dataset. arXiv (2019). https://doi.org/10.48550/ARXIV.1906.05750 . https://arxiv.org/abs/1906.05750 Liu et al. [2021] Liu, Y., Zhao, M., Zhang, Z., Zhang, H., Yan, S.: Arbitrary virtual try-on network: Characteristics preservation and trade-off between body and clothing. arXiv preprint arXiv:2111.12346 (2021) Jocher et al. [2021] Jocher, G., Stoken, A., Chaurasia, A., Borovec, J., NanoCode012, TaoXie, Kwon, Y., Michael, K., Changyu, L., Fang, J., V, A., Laughing, tkianai, yxNONG, Skalski, P., Hogan, A., Nadar, J., imyhxy, Mammana, L., AlexWang1900, Fati, C., Montes, D., Hajek, J., Diaconu, L., Minh, M.T., Marc, albinxavi, fatih, oleg, wanghaoyang0106: ultralytics/yolov5: v6.0 - YOLOv5n ’Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support. Zenodo (2021). https://doi.org/10.5281/zenodo.5563715 . https://doi.org/10.5281/zenodo.5563715 Lin et al. [2014] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Dong, H., Liang, X., Shen, X., Wu, B., Chen, B.-C., Yin, J.: Fw-gan: Flow-navigated warping gan for video virtual try-on. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1161–1170 (2019) Guo et al. [2019] Guo, S., Huang, W., Zhang, X., Srikhanta, P., Cui, Y., Li, Y., Scott, M.R., Adam, H., Belongie, S.: The iMaterialist Fashion Attribute Dataset. arXiv (2019). https://doi.org/10.48550/ARXIV.1906.05750 . https://arxiv.org/abs/1906.05750 Liu et al. [2021] Liu, Y., Zhao, M., Zhang, Z., Zhang, H., Yan, S.: Arbitrary virtual try-on network: Characteristics preservation and trade-off between body and clothing. arXiv preprint arXiv:2111.12346 (2021) Jocher et al. [2021] Jocher, G., Stoken, A., Chaurasia, A., Borovec, J., NanoCode012, TaoXie, Kwon, Y., Michael, K., Changyu, L., Fang, J., V, A., Laughing, tkianai, yxNONG, Skalski, P., Hogan, A., Nadar, J., imyhxy, Mammana, L., AlexWang1900, Fati, C., Montes, D., Hajek, J., Diaconu, L., Minh, M.T., Marc, albinxavi, fatih, oleg, wanghaoyang0106: ultralytics/yolov5: v6.0 - YOLOv5n ’Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support. Zenodo (2021). https://doi.org/10.5281/zenodo.5563715 . https://doi.org/10.5281/zenodo.5563715 Lin et al. [2014] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Guo, S., Huang, W., Zhang, X., Srikhanta, P., Cui, Y., Li, Y., Scott, M.R., Adam, H., Belongie, S.: The iMaterialist Fashion Attribute Dataset. arXiv (2019). https://doi.org/10.48550/ARXIV.1906.05750 . https://arxiv.org/abs/1906.05750 Liu et al. [2021] Liu, Y., Zhao, M., Zhang, Z., Zhang, H., Yan, S.: Arbitrary virtual try-on network: Characteristics preservation and trade-off between body and clothing. arXiv preprint arXiv:2111.12346 (2021) Jocher et al. [2021] Jocher, G., Stoken, A., Chaurasia, A., Borovec, J., NanoCode012, TaoXie, Kwon, Y., Michael, K., Changyu, L., Fang, J., V, A., Laughing, tkianai, yxNONG, Skalski, P., Hogan, A., Nadar, J., imyhxy, Mammana, L., AlexWang1900, Fati, C., Montes, D., Hajek, J., Diaconu, L., Minh, M.T., Marc, albinxavi, fatih, oleg, wanghaoyang0106: ultralytics/yolov5: v6.0 - YOLOv5n ’Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support. Zenodo (2021). https://doi.org/10.5281/zenodo.5563715 . https://doi.org/10.5281/zenodo.5563715 Lin et al. [2014] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. 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[2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Jocher, G., Stoken, A., Chaurasia, A., Borovec, J., NanoCode012, TaoXie, Kwon, Y., Michael, K., Changyu, L., Fang, J., V, A., Laughing, tkianai, yxNONG, Skalski, P., Hogan, A., Nadar, J., imyhxy, Mammana, L., AlexWang1900, Fati, C., Montes, D., Hajek, J., Diaconu, L., Minh, M.T., Marc, albinxavi, fatih, oleg, wanghaoyang0106: ultralytics/yolov5: v6.0 - YOLOv5n ’Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support. Zenodo (2021). https://doi.org/10.5281/zenodo.5563715 . https://doi.org/10.5281/zenodo.5563715 Lin et al. [2014] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. 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Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. 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[2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. 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[2014] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. 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Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Jocher, G., Stoken, A., Chaurasia, A., Borovec, J., NanoCode012, TaoXie, Kwon, Y., Michael, K., Changyu, L., Fang, J., V, A., Laughing, tkianai, yxNONG, Skalski, P., Hogan, A., Nadar, J., imyhxy, Mammana, L., AlexWang1900, Fati, C., Montes, D., Hajek, J., Diaconu, L., Minh, M.T., Marc, albinxavi, fatih, oleg, wanghaoyang0106: ultralytics/yolov5: v6.0 - YOLOv5n ’Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support. Zenodo (2021). https://doi.org/10.5281/zenodo.5563715 . https://doi.org/10.5281/zenodo.5563715 Lin et al. [2014] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. 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Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. 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[2014] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. 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[2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer Han et al. [2018] Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: Viton: An image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018) Wang et al. [2018] Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. 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[2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. 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International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. 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Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018) Minar et al. [2020] Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. 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[2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. 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[2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. 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Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. 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Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. 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[2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. 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[2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. 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Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. 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Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. 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In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. 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IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Minar, M.R., Tuan, T.T., Ahn, H., Rosin, P., Lai, Y.-K.: Cp-vton+: Clothing shape and texture preserving image-based virtual try-on. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020) Yu et al. [2019] Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. 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[2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Yu, R., Wang, X., Xie, X.: Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10511–10520 (2019) Ayush et al. [2019] Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. 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IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. 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International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. [2014] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Networks. arXiv (2014). https://doi.org/10.48550/ARXIV.1406.2661 . https://arxiv.org/abs/1406.2661 Russell et al. [2008] Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. International journal of computer vision 77(1), 157–173 (2008) Kingma and Ba [2014] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv (2014). https://doi.org/10.48550/ARXIV.1412.6980 . https://arxiv.org/abs/1412.6980 Zhao et al. [2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Ayush, K., Jandial, S., Chopra, A., Krishnamurthy, B.: Powering virtual try-on via auxiliary human segmentation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019) Krizhevsky et al. [2017] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. 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[2020] Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H.: Image Augmentations for GAN Training. arXiv (2020). https://doi.org/10.48550/ARXIV.2006.02595 . https://arxiv.org/abs/2006.02595 Jung et al. [2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6), 84–90 (2017) Simonyan and Zisserman [2014] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. 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[2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Huang et al. [2017] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) Goodfellow et al. 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[2020] Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. 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[2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020)
  21. Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al.: imgaug. https://github.com/aleju/imgaug. Online; accessed 01-Feb-2020 (2020) Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020)
  22. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017) Bińkowski et al. [2018] Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. 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[2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. 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[2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. 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  24. Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. arXiv preprint arXiv:1801.01401 (2018) Gupta et al. [2019] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020)
  25. Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019) Chen et al. [2014] Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020)
  26. Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014) Lin et al. [2020] Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020)
  27. Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.-T.: Cross-domain complementary learning using pose for multi-person part segmentation. IEEE Transactions on Circuits and Systems for Video Technology 31(3), 1066–1078 (2020) Gong et al. [2019] Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: Universal human parsing via graph transfer learning. In: CVPR (2019) Qin et al. [2020] Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020) Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020)
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  29. Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection, vol. 106, p. 107404 (2020)
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