Deep High-Resolution Representation Learning for Visual Recognition

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 43; no. 10; pp. 3349 - 3364
• Main Authors: Wang, Jingdong, Sun, Ke, Cheng, Tianheng, Jiang, Borui, Deng, Chaorui, Zhao, Yang, Liu, Dong, Mu, Yadong, Tan, Mingkui, Wang, Xinggang, Liu, Wenyu, Xiao, Bin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer vision; Convolutional codes; High resolution; high-resolution representations; HRNet; human pose estimation; Image resolution; Image segmentation; Indexes; low-resolution representations; Object detection; Object recognition; Pose estimation; Position sensing; Representations; Semantic segmentation; Semantics; Spatial resolution

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 b...