| # Learning delicate local representations for multi-person pose estimation | |
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| <details> | |
| <summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-030-58580-8_27">RSN (ECCV'2020)</a></summary> | |
| ```bibtex | |
| @misc{cai2020learning, | |
| title={Learning Delicate Local Representations for Multi-Person Pose Estimation}, | |
| author={Yuanhao Cai and Zhicheng Wang and Zhengxiong Luo and Binyi Yin and Angang Du and Haoqian Wang and Xinyu Zhou and Erjin Zhou and Xiangyu Zhang and Jian Sun}, | |
| year={2020}, | |
| eprint={2003.04030}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |
| </details> | |
| ## Abstract | |
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| In this paper, we propose a novel method called Residual Steps Network (RSN). RSN aggregates features with the same spatial size (Intra-level features) efficiently to obtain delicate local representations, which retain rich low-level spatial information and result in precise keypoint localization. Additionally, we observe the output features contribute differently to final performance. To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations. Our approach won the 1st place of COCO Keypoint Challenge 2019 and achieves state-of-the-art results on both COCO and MPII benchmarks, without using extra training data and pretrained model. Our single model achieves 78.6 on COCO test-dev, 93.0 on MPII test dataset. Ensembled models achieve 79.2 on COCO test-dev, 77.1 on COCO test-challenge dataset. | |
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| <div align=center> | |
| <img src="https://user-images.githubusercontent.com/15977946/146522226-5041d16e-41cb-4d31-ae53-dbe21314a697.png"> | |
| </div> | |