Transcription of IEEE TRANSACTIONS ON PATTERN ANALYSIS AND …
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND …
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, MARCH 20201 Deep High-Resolution Representation Learningfor Visual RecognitionJingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu,Mingkui Tan, Xinggang Wang, Wenyu Liu, and Bin XiaoAbstract 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-resolutionrepresentation through a subnetwork that is formed by connecting high-to-low resolution convolutionsin series( , ResNet,VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposednetwork, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There aretwo key characteristics: (i) Connect the high-to-low resolution convolution streamsin parallel; (ii) Repeatedly exchange the informationacross resolutions.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, MARCH 2020 2 (a) (b) Fig. 1. The structure of recovering high resolution from low resolution. (a) A low-resolution representation learning subnetwork (such as VGGNet [126], ResNet [54]), which is formed by connecting high-to-low convolutions in series.
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