| # Rethinking on multi-stage networks for human pose estimation | |
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| <details> | |
| <summary align="right"><a href="https://arxiv.org/abs/1901.00148">MSPN (ArXiv'2019)</a></summary> | |
| ```bibtex | |
| @article{li2019rethinking, | |
| title={Rethinking on Multi-Stage Networks for Human Pose Estimation}, | |
| author={Li, Wenbo and Wang, Zhicheng and Yin, Binyi and Peng, Qixiang and Du, Yuming and Xiao, Tianzi and Yu, Gang and Lu, Hongtao and Wei, Yichen and Sun, Jian}, | |
| journal={arXiv preprint arXiv:1901.00148}, | |
| year={2019} | |
| } | |
| ``` | |
| </details> | |
| ## Abstract | |
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| Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multi-stage methods are seemingly more suited for the task, their performance in current practice is not as good as single-stage methods. This work studies this issue. We argue that the current multi-stage methods' unsatisfactory performance comes from the insufficiency in various design choices. We propose several improvements, including the single-stage module design, cross stage feature aggregation, and coarse-to-fine supervision. The resulting method establishes the new state-of-the-art on both MS COCO and MPII Human Pose dataset, justifying the effectiveness of a multi-stage architecture. The source code is publicly available for further research. | |
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| <img src="https://user-images.githubusercontent.com/15977946/146520722-74c1ef85-9fa3-4b96-8cbf-4a2c86a80adc.png"> | |
| </div> | |