{"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/190807919","title":"Deep High-Resolution Representation Learning for Visual Recognition","arxiv_id":"1908.07919","date":"2019-08-20","proceeding":null,"authors":["Jingdong Wang","Ke Sun","Tianheng Cheng","Borui Jiang","Chaorui Deng","Yang Zhao","Dong Liu","Yadong Mu","Mingkui Tan","Xinggang Wang","Wenyu Liu","Bin Xiao"],"abstract":"High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions \\emph{in series} (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams \\emph{in parallel}; (ii) Repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at~{\\url{https://github.com/HRNet}}.","url_abs":"https://arxiv.org/abs/1908.07919v2","url_pdf":"https://arxiv.org/pdf/1908.07919v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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