K
Ke Sun
Researcher at University of Science and Technology of China
Publications - 13
Citations - 6299
Ke Sun is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Pose & Feature learning. The author has an hindex of 8, co-authored 13 publications receiving 2493 citations.
Papers
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Proceedings ArticleDOI
Deep High-Resolution Representation Learning for Human Pose Estimation
TL;DR: This paper proposes a network that maintains high-resolution representations through the whole process of human pose estimation and empirically demonstrates the effectiveness of the network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.
Posted Content
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 +11 more
TL;DR: The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
Journal ArticleDOI
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 +11 more
TL;DR: The High-Resolution Network (HRNet) as mentioned in this paper maintains high-resolution representations through the whole process by connecting the high-to-low resolution convolution streams in parallel and repeatedly exchanging the information across resolutions.
Posted Content
High-Resolution Representations for Labeling Pixels and Regions
Ke Sun,Yang Zhao,Borui Jiang,Tianheng Cheng,Bin Xiao,Dong Liu,Yadong Mu,Xinggang Wang,Wenyu Liu,Jingdong Wang +9 more
TL;DR: A simple modification is introduced to augment the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions rather than only the representation from thehigh-resolution convolution, which leads to stronger representations, evidenced by superior results.
Proceedings ArticleDOI
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
TL;DR: In this paper, the authors proposed a disentangled keypoint regression (DEKR) method, which adopts adaptive convolutions through pixel-wise spatial transformer to activate the pixels in the keypoint regions and accordingly learn representations from them.