| # Deep high-resolution representation learning for human pose estimation | |
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| <details> | |
| <summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary> | |
| ```bibtex | |
| @inproceedings{sun2019deep, | |
| title={Deep high-resolution representation learning for human pose estimation}, | |
| author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong}, | |
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
| pages={5693--5703}, | |
| year={2019} | |
| } | |
| ``` | |
| </details> | |
| ## Abstract | |
| <!-- [ABSTRACT] --> | |
| In this paper, we are interested in the human pose estimation problem with a focus on learning reliable highresolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutliresolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich highresolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness | |
| of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection | |
| dataset and the MPII Human Pose dataset. In addition, we show the superiority of our network in pose tracking on the PoseTrack dataset. | |
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| <div align=center> | |
| <img src="https://user-images.githubusercontent.com/15977946/146518010-9c1d6078-dd9c-4610-94a9-cbcb60aa87c0.png"> | |
| </div> | |