The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation
Open accessAbstract
Recently, the leading performance of human pose estimation is dominated by top-down methods. Being a fundamental component in training and inference, data processing has not been systematically considered in pose estimation community, to the best of our knowledge. In this paper, we focus on this problem and find that the devil of top-down pose estimator is in the biased data processing. Specifically, by investigating the standard data processing in state-of-the-art approaches mainly including data transformation and encoding-decoding, we find that the results obtained by common flipping strategy are unaligned with the original ones in inference. Moreover, there is statistical error in standard encoding-decoding during both training and inference. Two problems couple together and significantly degrade the pose estimation performance. Based on quantitative analyses, we then formulate a principled way to tackle this dilemma. Data is processed in continuous space based on unit length (the intervals between pixels) instead of in discrete space with pixel, and a combined classification and regression approach is adopted to perform encoding-decoding. The Unbiased Data Processing (UDP) for human pose estimation can be achieved by combining the two together. UDP not only boosts the performance of existing methods by a large margin but also plays a important role in result reproducing and future exploration. As a model-agnostic approach, UDP promotes SimpleBaseline-ResNet50-256x192 by 1.5 AP (70.2 to 71.7) and HRNet-W32-256x192 by 1.7 AP (73.5 to 75.2) on COCO test-dev set. The HRNet-W48-384x288 equipped with UDP achieves 76.5 AP and sets a new state-of-the-art for human pose estimation. The source code is publicly available for further research.
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Pose Recognition with Cascade Transformers
Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., & Tu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00198 · Cited by 301
HRFormer: High-Resolution Transformer for Dense Prediction
Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X., & Wang, J. (2021) · arXiv · doi:10.48550/arxiv.2110.09408 · Cited by 178
Spatial-Aware Regression for Keypoint Localization
Wang, D., & Zhang, S. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00066 · Cited by 12
Metadata
- Authors
- Huang, J., Zhu, Z., Guo, F., & Huang, G.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 5699-5708
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034742259
- Cited by
- 281
- References
- 39
- FWCI
- 11.44
- Type
- conference paper
- Language
- en
- Open access
- green
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