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25 April 2024 |
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Article overview
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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
; | | Date: |
20 Aug 2019 | | 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{this https URL}}. | | Source: | arXiv, 1908.7919 | | Services: | Forum | Review | PDF | Favorites | |
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