Yansong Tang

I am a tenure-track Assistant Professor of Tsinghua-Berkeley Shenzhen Institute/Shenzhen International Graduate School, Tsinghua University, where I direct the IVG@SZ (Intelligent Vision Group at Shenzhen, the sister group of the IVG at Beijing). Before that, I was a postdoctoral researcher at the Department of Engineering Science of the University of Oxford, working with Prof. Philip H. S. Torr and Prof. Victor Prisacariu. My current research interests lie in computer vision, computer graphics and machine learning.

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

I am looking for self-motivated Master/PhD/Postdoc. If you have top grades or coding skill, and are highly creative and interested in joining my group, please do not hesitate to send me your CV and transcripts of grades by Email after reading this file.

(*Equal Contribution, #Corresponding Author)

We present FLAG3D, a large-scale 3D fitness activity dataset with language instruction.

LOGO is a new multi-person long-form video dataset for action quality assessment.

HorNet is a family of generic vision backbones that perform explicit high-order spatial interactions based on Recursive Gated Convolution.

We present a language-powered paradigm for ordinal regression.

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

We present Bi-level Neural Volume Fusion, which leverages recent advances in neural implicit representations and neural rendering for dense 3D reconstruction. In order to incrementally integrate new depth maps into a global neural implicit representation, we propose a novel bi-level fusion strategy that considers both efficiency and reconstruction quality by design.

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

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

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

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