G
Gao Huang
Researcher at Tsinghua University
Publications - 164
Citations - 43663
Gao Huang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 37, co-authored 124 publications receiving 26697 citations. Previous affiliations of Gao Huang include Cornell University & University of Science and Technology of China.
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Horizontal Pyramid Matching for Person Re-identification
Yang Fu,Yunchao Wei,Yuqian Zhou,Honghui Shi,Gao Huang,Xinchao Wang,Zhiqiang Yao,Thomas S. Huang +7 more
TL;DR: Wang et al. as discussed by the authors proposed a simple yet effective horizontal pyramid matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can still be still identified even even some key parts are missing.
Posted Content
Cross-Iteration Batch Normalization
TL;DR: Cross-Iteration Batch Normalization (CBN) is presented, in which examples from multiple recent iterations are jointly utilized to enhance estimation quality and outperform the original batch normalization and a direct calculation of statistics over previous iterations without the proposed compensation technique.
Journal ArticleDOI
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning.
TL;DR: A self-supervised interpretable framework is proposed, which can discover interpretable features to enable easy understanding of RL agents even for non-experts and provides valuable insight into the internal decision-making process of vision-based RL.
Journal ArticleDOI
FSD-10: A Fine-grained Classification Dataset for Figure Skating
TL;DR: A keyframe based temporal segment network (KTSN) for classification and achieve remarkable performance is proposed by the idea that domain knowledge is of great concern in sports field and is designed to have a large collection of finegrained actions.
Book ChapterDOI
Transductive Minimax Probability Machine
TL;DR: It is shown that the proposed Transductive MPM (TMPM) almost outperforms all the other algorithms in both accuracy and speed.