Papers
Topics
Authors
Recent
Search
2000 character limit reached

Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection

Published 2 Jan 2024 in cs.CV | (2401.01134v1)

Abstract: This paper introduces HPC-Net, a high-precision and rapidly convergent object detection network.

Authors (4)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (47)
  1. Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (idw). Computers and Geosciences, 22(7), 1996.
  2. An overview of deep learning based object detection techniques. In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pages 1–6, 2019.
  3. Do-conv: Depthwise over-parameterized convolutional layer. IEEE Transactions on Image Processing, 31, 2023.
  4. Multi-view 3d object detection network for autonomous driving. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6526–6534, 2017.
  5. François Chollet. Xception: Deep learning with depthwise separable convolutions. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1800–1807, 2017.
  6. Deformable convolutional networks. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 764–773, 2017.
  7. Voxel r-cnn: Towards high performance voxel-based 3d object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2), 2021.
  8. Lee R. Dice. Measures of the amount of ecologic association between species. Ecology, 26(3), 1945.
  9. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354–3361, 2012.
  10. Ross Girshick. Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015.
  11. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 2015.
  12. Penet: Towards precise and efficient image guided depth completion. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13656–13662, 2021.
  13. Logonet: Towards accurate 3d object detection with local-to-global cross-modal fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17524–17534, 2023.
  14. Feature pyramid networks for object detection. IEEE Computer Society, 2017.
  15. Ssd: Single shot multibox detector. In Computer Vision – ECCV 2016, pages 21–37, 2016.
  16. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10012–10022, 2021.
  17. Swin transformer v2: Scaling up capacity and resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12009–12019, 2022.
  18. An adaptive inverse-distance weighting spatial interpolation technique. Computers and Geosciences, 34(9), 2008.
  19. Efficient non-maximum suppression. In 18th International Conference on Pattern Recognition (ICPR’06), pages 850–855, 2006.
  20. Object detection techniques: Overview and performance comparison. In 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pages 1–5, 2019.
  21. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017a.
  22. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 5105–5114, 2017b.
  23. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788, 2016.
  24. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems, 2015.
  25. Improving 3d object detection with channel-wise transformer. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 2723–2732, 2021.
  26. Donald Shepard. A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM National Conference, pages 517–524, 1968.
  27. Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10526–10535, 2020.
  28. T. SORENSEN. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biologiske Skrifter, 5, 1948.
  29. Adapool: Exponential adaptive pooling for information-retaining downsampling. IEEE Transactions on Image Processing, 32, 2023.
  30. Refining activation downsampling with softpool. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10357–10366, 2021.
  31. Scalability in perception for autonomous driving: Waymo open dataset. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2443–2451, 2020.
  32. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 6000–6010, 2017.
  33. Casa: A cascade attention network for 3-d object detection from lidar point clouds. IEEE Transactions on Geoscience and Remote Sensing, 60, 2022a.
  34. Transformation-equivariant 3d object detection for autonomous driving. Proceedings of the AAAI Conference on Artificial Intelligence, 37(3), 2023a.
  35. Virtual sparse convolution for multimodal 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21653–21662, 2023b.
  36. Sparse fuse dense: Towards high quality 3d detection with depth completion. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5408–5417, 2022b.
  37. 3-d hanet: A flexible 3-d heatmap auxiliary network for object detection. IEEE Transactions on Geoscience and Remote Sensing, 61, 2023.
  38. Second: Sparsely embedded convolutional detection. Sensors, 18(10), 2018.
  39. Graph r-cnn: Towards accurate 3d object detection with semantic-decorated local graph. In Computer Vision – ECCV 2022, pages 662–679, 2022.
  40. 3d-cvf: Generating joint camera and lidar features using cross-view spatial feature fusion for 3d object detection. In Computer Vision – ECCV 2020, pages 720–736, 2020.
  41. A comprehensive study of the robustness for lidar-based 3d object detectors against adversarial attacks. International Journal of Computer Vision, 2023a.
  42. Unleash the potential of image branch for cross-modal 3d object detection. In Advances in Neural Information Processing Systems, 2023b.
  43. Glenet: Boosting 3d object detectors with generative label uncertainty estimation. International Journal of Computer Vision, pages 1–21, 2023c.
  44. Spatial-temporal enhanced transformer towards multi-frame 3d object detection. arXiv preprint arXiv:2307.00347, 2023d.
  45. Se-ssd: Self-ensembling single-stage object detector from point cloud. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14494–14503, 2021.
  46. Voxelnet: End-to-end learning for point cloud based 3d object detection. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4490–4499, 2018.
  47. Vpfnet: Improving 3d object detection with virtual point based lidar and stereo data fusion. IEEE Transactions on Multimedia, 2022.

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.