Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation (1907.05193v2)

Abstract: Supervised deep learning with pixel-wise training labels has great successes on multi-person part segmentation. However, data labeling at pixel-level is very expensive. To solve the problem, people have been exploring to use synthetic data to avoid the data labeling. Although it is easy to generate labels for synthetic data, the results are much worse compared to those using real data and manual labeling. The degradation of the performance is mainly due to the domain gap, i.e., the discrepancy of the pixel value statistics between real and synthetic data. In this paper, we observe that real and synthetic humans both have a skeleton (pose) representation. We found that the skeletons can effectively bridge the synthetic and real domains during the training. Our proposed approach takes advantage of the rich and realistic variations of the real data and the easily obtainable labels of the synthetic data to learn multi-person part segmentation on real images without any human-annotated labels. Through experiments, we show that without any human labeling, our method performs comparably to several state-of-the-art approaches which require human labeling on Pascal-Person-Parts and COCO-DensePose datasets. On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin. We further demonstrate the generalizability of our method on predicting novel keypoints in real images where no real data labels are available for the novel keypoints detection. Code and pre-trained models are available at https://github.com/kevinlin311tw/CDCL-human-part-segmentation

Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation: An Overview

In the pursuit of advancing multi-person part segmentation, the paper entitled "Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation" proposes an innovative method leveraging both synthetic and real-world data to circumvent the high costs and scalability issues associated with pixel-wise data labeling. Traditional supervised deep learning techniques, while successful, demand extensive human-annotated datasets, which are costly and labor-intensive to obtain. Hence, this paper introduces a framework that capitalizes on the skeleton (pose) representations inherent in both synthetic and real datasets as a bridge to mitigate domain discrepancies.

Methodological Advancements

The core premise of this work revolves around the novel application of cross-domain complementary learning (CDCL) to effectively harness complementary information from both synthetic and real-world domains. Critically, the method utilizes pose estimation as an auxiliary task, serving as a shared representational framework that facilitates knowledge transfer between domains. This approach not only enhances the learning process but also obviates the need for direct human labeling of real-world part segmentation data.

The authors employ a comprehensive network architecture comprising a ResNet101 backbone with pyramid connections, along with multiple head networks dedicated to predicting part segmentation maps, confidence keypoint maps, and Part Affinity Fields (PAFs). This configuration enables simultaneous learning of part segmentation and pose estimation, thereby ensuring effective alignment of the feature spaces across different data domains.

Empirical Evidence and Results

The framework's performance was rigorously evaluated against several benchmarks, specifically the Pascal-Person-Parts and COCO-DensePose datasets. Without using any human-annotated part segmentation labels, the CDCL technique remarkably achieves competitive performance to state-of-the-art supervised methods, demonstrating its efficacy and robustness. Quantitative assessments reveal that the proposed method performs comparably to or surpasses existing approaches, particularly notable when part labels from real images are incorporated during training phases.

Furthermore, the research explores the flexibility of the proposed method through the task of novel keypoint detection. By generating synthetic images with additional keypoints, the model successfully transfers learned knowledge to predict unseen keypoints in real images without requiring manually labeled datasets for these points.

Practical and Theoretical Implications

The implications of this paper are significant for both practical application and theoretical understanding. Practically, the method provides a cost-effective solution for multi-person part segmentation, reducing reliance on costly labeled datasets while still achieving high segmentation accuracy. Theoretically, the introduction of skeleton-based domain bridging expands our understanding of cross-domain learning, illustrating the potential for leveraging pose estimation as a universal feature space that significantly reduces reality gaps between synthetic and real-world data.

Future Directions and Speculation

Future research can build upon these findings by exploring more nuanced domain adaptation techniques or integrating this framework into real-time systems for further applications in human-centric analysis tasks. Additionally, investigating the combination of CDCL with other machine learning paradigms, such as adversarial learning or reinforcement learning, could potentially enhance the robustness and scalability of segmentation systems across more diverse and dynamic environments.

In conclusion, the paper presents a thorough and methodologically sound approach to addressing the challenges of multi-person part segmentation with limited reliance on human annotations, fostering a path towards more scalable and efficient AI systems in visual recognition tasks.