Y
Yang Zhao
Researcher at Griffith University
Publications - 19
Citations - 3124
Yang Zhao is an academic researcher from Griffith University. The author has contributed to research in topics: Computer science & Feature learning. The author has an hindex of 7, co-authored 13 publications receiving 1089 citations. Previous affiliations of Yang Zhao include University of Adelaide.
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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 +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.
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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.
Journal ArticleDOI
MobileFAN: Transferring deep hidden representation for face alignment
TL;DR: An effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder is proposed, which achieves superior or equivalent performance compared with state-of-the-art models.
Journal ArticleDOI
MaskCOV: A random mask covariance network for ultra-fine-grained visual categorization
TL;DR: Experimental results of the proposed novel random mask covariance network (MaskCOV), which integrates an auxiliary self-supervised learning module with a powerful in-image data augmentation scheme for the ultra-FGVC, demonstrate its superiority and potential of MaskCOV concept, which pushes research boundary forward from the fine-grained to theUltra-fine-Grained visual categorization.