2019

Deep High-Resolution Representation Learning for Visual Recognition

Wang, Jingdong, Sun, Ke, Cheng, Tianheng et al.

Understand

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.

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