Deep High-Resolution Representation Learning for Visual Recognition

Jingdong Wang; Ke Sun; Tianheng Cheng; Borui Jiang; Chaorui Deng; Yang Zhao; Dong Liu; Yadong Mu; Mingkui Tan; Xinggang Wang; Wenyu Liu; Bin Xiao

arXiv · 1908.07919

Deep High-Resolution Representation Learning for Visual Recognition

Jingdong Wang·Ke Sun·Tianheng Cheng·Borui Jiang·Chaorui Deng·Yang Zhao·Dong Liu·Yadong Mu·Mingkui Tan·Xinggang Wang·Wenyu Liu·Bin Xiao

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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}}.

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BibTeX RIS

Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao. 2020-03-13. Deep High-Resolution Representation Learning for Visual Recognition. https://arxiv.org/abs/1908.07919

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