Learning Delicate Local Representations for Multi-Person Pose Estimation

• URL: http://arxiv.org/abs/2003.04030v3
• Date: Wed, 15 Jul 2020 13:09:57 GMT
• Title: Learning Delicate Local Representations for Multi-Person Pose Estimation
• Authors: Yuanhao Cai, Zhicheng Wang, Zhengxiong Luo, Binyi Yin, Angang Du, Haoqian Wang, Xiangyu Zhang, Xinyu Zhou, Erjin Zhou, Jian Sun
• Abstract summary: 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. Our approach won the 1st place of COCO Keypoint Challenge 2019.
• Score: 77.53144055780423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. The source code is publicly available for further research at https://github.com/caiyuanhao1998/RSN/

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