  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/
