Yantao SHEN, 沈岩涛

PhD,
Electronic Engineering Department,
The Chinese University of Hong Kong (CUHK)
E-mail: ytshen [@] link [DOT] cuhk [DOT] edu [DOT] hk

About Me

I obtained my Ph.D. degree in Electronic Engineering from the Department of Electronic Engineering and Multimedia Laboratory (MMLAB), The Chinese University of Hong Kong, supervised by Prof. Xiaogang Wang and Prof. Hongsheng Li . I also work closly with Dr. Tong Xiao, Dr. Yuanjun Xiong, and Dr. Wei Xia. I received my B.E. degree from the Department of Automation, Northeastern University, China, in 2015.

My CV, Google Scholar, Github

Research Interest

My research interests include

News

Recent Publications

Y. Shen, Y. Xiong, W. Xia, S. Soatto, "Towards Backward-Compatible Representation Learning", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Oral) [PDF link] [Press Coverage] [Code]

Y. Shen, H. Li, T. Xiao, S. Yi, D. Chen, X. Wang, "Person Re-identification with Deep Kronecker-Product Matching and Group-shuffling Random Walk", IEEE Transactions on Pattern Analysis and Machine Intelligence, (TPAMI). [link]

Y. Shen, H. Li, S. Yi, D. Chen, X. Wang, "Person Re-identification with Deep Similarity-Guided Graph Neural Network", 15th European Conference on Computer Vision (ECCV), 2018. [PDF link]

D. Chen, H. Li, X, Liu, Y. Shen, J. Shao, Z. Yuan, X. Wang, "Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association", 15th European Conference on Computer Vision (ECCV), 2018. [PDF link]

Y. Shen, H. Li, S. Yi, D. Chen, X. Wang, "Deep Group-shuffling Random Walk for Person Re-identification ", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [PDF link] [Code]

Y. Shen^, T. Xiao^, H. Li, S. Yi, X. Wang, " End-to-End Deep Kronecker-Product Matching for Person Re-identification ", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [PDF link] [Code] (^denotes co-first authors)

Y. Shen, T. Xiao, H. Li, S. Yi, X. Wang, " Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals ", IEEE International Conference on Computer Vision (ICCV), 2017. [PDF link]