Yansong Tang

I am currently a Sponsored Postdoctoral Researcher in the Department of Engineering Science at the University of Oxford, working with Prof. Philip H. S. Torr. My research interests lie in computer vision. Currently, I am working towards learning data-efficient and interpretable representations for video understanding.

Prior to that, I received my Ph.D degree at Tsinghua University, advised by Prof. Jie Zhou and Prof. Jiwen Lu, and B.S. degree in Automation from Tsinghua University. I have also spent time at Visual Computing Group of Microsoft Research Asia (MSRA), and Prof. Song-Chun Zhu’s VCLA lab of University of California, Los Angeles (UCLA).

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* indicates equal contribution

We observe a collapse phenomenon when directly applying fully convolutional cycle-consistency method for video correspondence learning, study the underline reason behind it, and propose a spatial transformation approach to address this issue.

We propose an uncertainty-aware score distribution learning method and extend it to a multi-path model for action quality assessment.

Journal version of the COIN dataset.

We propose a graph interaction networks (GINs) model for transferring relation knowledge across two graphs two different scenarios for video analysis, including a new proposed setting for unsupervised skeleton-based action recognition across different datasets, and supervised group activity recognition with multi-modal inputs.

We extend of our Semantics-Preserving Attention model with graph convolutional module for group activity recognition.

COIN is one of the largest and most comprehensive instructional video analysis datasets with rich annotations.

We propose a multi-stream deep neural networks and THU-READ dataset for RGB-D egocentric action recognition.

We present a simple yet effective semantics-preserving attention module for group activity recognition.

We propose a simple yet effective method to select key frames for skeleton-based action recognition using the REINFORCE algorithm.

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