{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bottom-up-higher-resolution-networks-for","title":"HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation","arxiv_id":"1908.10357","date":"2019-08-27","proceeding":"CVPR 2020 6","authors":["Bowen Cheng","Bin Xiao","Jingdong Wang","Honghui Shi","Thomas S. Huang","Lei Zhang"],"abstract":"Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. 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