Xumin Yu

I am a fifth year Ph.D student in the Department of Automation at Tsinghua University, advised by Prof. Jiwen Lu. In 2020, I obtained my B.Eng. in the Department of Electronic Engineering, Tsinghua University.

I am broadly interested in computer vision and deep learning. My current research focuses on 3D vision and Video analysis.

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Publications

* indicates equal contribution

P2P is a framework to leverage large-scale pre-trained image models for 3D point cloud analysis.

Point-BERT is a new paradigm for learning Transformers in an unsupervised manner by generalizing the concept of BERT onto 3D point cloud data.

PoinTr is a transformer-based framework that reformulates point cloud completion as a set-to-set translation problem.

We propose a new contrastive regression (CoRe) framework to learn the relative scores by pair-wise comparison, which highlights the differences between videos and guides the models to learn the key hints for 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.

Our method allows machine learning algorithms to perform fine-grained estimation of physiological states (e.g., sleep depth) even if the training labels are coarse-grained.

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