{"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/high-resolution-representations-for-labeling","title":"High-Resolution Representations for Labeling Pixels and Regions","arxiv_id":"1904.04514","date":"2019-04-09","proceeding":null,"authors":["Ke Sun","Yang Zhao","Borui Jiang","Tianheng Cheng","Bin Xiao","Dong Liu","Yadong Mu","Xinggang Wang","Wenyu Liu","Jingdong Wang"],"abstract":"High-resolution representation learning plays an essential role in many\nvision problems, e.g., pose estimation and semantic segmentation. The\nhigh-resolution network (HRNet)~\\cite{SunXLW19}, recently developed for human\npose estimation, maintains high-resolution representations through the whole\nprocess by connecting high-to-low resolution convolutions in \\emph{parallel}\nand produces strong high-resolution representations by repeatedly conducting\nfusions across parallel convolutions.\n  In this paper, we conduct a further study on high-resolution representations\nby introducing a simple yet effective modification and apply it to a wide range\nof vision tasks. We augment the high-resolution representation by aggregating\nthe (upsampled) representations from all the parallel convolutions rather than\nonly the representation from the high-resolution convolution as done\nin~\\cite{SunXLW19}. This simple modification leads to stronger representations,\nevidenced by superior results. We show top results in semantic segmentation on\nCityscapes, LIP, and PASCAL Context, and facial landmark detection on AFLW,\nCOFW, $300$W, and WFLW. In addition, we build a multi-level representation from\nthe high-resolution representation and apply it to the Faster R-CNN object\ndetection framework and the extended frameworks. The proposed approach achieves\nsuperior results to existing single-model networks on COCO object detection.\nThe code and models have been publicly available at\n\\url{https://github.com/HRNet}.","url_abs":"http://arxiv.org/abs/1904.04514v1","url_pdf":"http://arxiv.org/pdf/1904.04514v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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