Yansong Tang

I am currently a Postdoctoral Researcher in the Department of Engineering Science at the University of Oxford, working with Prof. Philip H. S. Torr and Prof. Victor Prisacariu. My research interests lie in computer vision. Currently, I am working in the fields of video understanding and 3D reconstruction.

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).

Our group at the University of Oxford is looking for self-motivated interns and visitors to work on research projects related to 3D reconstruction and video understanding. If you are interested in joining my group at Oxford, please do not hesitate to drop me an email with your resume. Remote collaboration is also welcome.

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* indicates equal contribution

We present an end-to-end hierarchical Transformer-based network for referring segmentation.

DenseCLIP is a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP.

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 present an end-to-end hierarchical interaction network for video object segmentation from referring expressions, which leverages the feature pyramid produced by the visual encoder to generate multiple levels of multi-modal features.

Journal version of the COIN dataset.

We devise a temporal-spatial Cubism strategy, which guides the network to be aware of the permutation of the segments in the temporal domain and the body parts in the spatial domain separately, thus improves the generalization ability of the model for cross-dataset action recognition.

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

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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