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  • Deep High-Resolution Representation Learning for Visual Recognition

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    Zhao457045-Accepted.pdf (8.640Mb)
    Author(s)
    Wang, Jingdong
    Sun, Ke
    Cheng, Tianheng
    Jiang, Borui
    Deng, Chaorui
    Zhao, Yang
    Liu, Dong
    Mu, Yadong
    Tan, Mingkui
    Wang, Xinggang
    Liu, Wenyu
    Xiao, Bin
    Griffith University Author(s)
    Zhao, Yang
    Year published
    2020
    Metadata
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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 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 ...
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    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 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 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 https://github.com/HRNet.
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    Journal Title
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    DOI
    https://doi.org/10.1109/tpami.2020.2983686
    Copyright Statement
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Artificial Intelligence and Image Processing
    Information Systems
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/400575
    Collection
    • Journal articles

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