Xumin Yu

I am a second 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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News

  • 2021-07: 2 papers (including 1 oral) on 3D vision and video understanding are accepted to ICCV 2021.
  • Publications

    * indicates equal contribution

    dise PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
    Xumin Yu*, Yongming Rao *, Ziyi Wang, Zuyan Liu, Jiwen Lu , Jie Zhou
    IEEE International Conference on Computer Vision (ICCV), 2021
    Oral Presentation
    [arXiv] [supp] [Code] [中文解读 (by CVer)]

    PoinTr is a transformer-based model for point cloud completion. By representing the point cloud as a set of unordered groups of points with position embeddings, we convert the point cloud to a sequence of point proxies and employ a transformer encoder-decoder architecture for generation. We also propose two more challenging benchmarks ShapeNet-55/34 with more diverse incomplete point clouds that can better reflect the real-world scenarios to promote future research

    dise Group-aware Contrastive Regression for Action Quality Assessment
    Xumin Yu*, Yongming Rao*, Wenliang Zhao, Jiwen Lu , Jie Zhou
    IEEE International Conference on Computer Vision (ICCV), 2021
    [arXiv] [Code]

    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.

    dise Graph Interaction Networks for Relation Transfer in Human Activity Videos
    Yansong Tang, Yi Wei , Xumin Yu, Jiwen Lu , Jie Zhou
    IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020
    [Paper]

    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.

    dise Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
    Zengyi Qin , Jiansheng Chen , Zhenyu Jiang , Xumin Yu, Chunhua Hu, Yu Ma, Suhua Miao and Rongsong Zhou
    Scientific Reports , 2020
    [Paper] [Code]

    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.

    Honors and Awards

  • Excellent Undergraduate in Tsinghua University, 2020
  • The First Prize of Microsoft Imagine Cup, China Finals, 2018

  • Website Template


    © Yu Xumin | Last updated: August 3, 2021