Yongming Rao

I am a third year Ph.D student in the Department of Automation at Tsinghua University, advised by Prof. Jiwen Lu . In 2018, 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 data/computation-efficient deep learning methods and 3D vision.

Email  /  CV  /  Google Scholar  /  Github

profile photo
News

  • 2021-05: I was selected as an Outstanding Reviewer of CVPR 2021.
  • 2021-03: One paper on 3D scene flow estimation is accepted to CVPR 2021.
  • 2020-12: One paper on image classification is accepted to AAAI 2021.
  • 2020-08: I was selected as an Outstanding Reviewer of ECCV 2020.
  • 2020-07: Two papers on knowledge distillation and person re-identification are accepted to ECCV 2020.
  • 2020-06: Our team (I and Guangyi) won the 2nd place in Semi-Supervised Recognition Challenge at FGVC7 (CVPR 2020).
  • 2020-02: Three papers on unsupervised 3D understanding and image/face super-resoluation are accepted to CVPR 2020.
  • 2019-06: I co-organized the Tutorial on Deep Reinforcement Learning for Computer Vision at CVPR 2019.
  • 2019-02: Two papers on robust point cloud analysis and instructional video understanding are accepted to CVPR 2019.
  • Publications

    * indicates equal contribution

    dise DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
    Yongming Rao, Wenliang Zhao, Benlin Liu , Jiwen Lu , Jie Zhou , Cho-Jui Hsieh
    Preprint, 2021
    [arXiv] [Code] [Project Page] [知乎]

    We present a dynamic token sparsification framework to prune redundant tokens in vision transformers progressively and dynamically based on the input.

    dise PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
    Yi Wei *, Ziyi Wang*, Yongming Rao*, Jiwen Lu , Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
    [arXiv] [Code]

    We present point-voxel correlation fields for 3D scene flow estimation which migrates the high performance of RAFT and provides a solution to build structured all-pairs correlation fields for unstructured point clouds.

    dise Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
    Yongming Rao, Jiwen Lu , Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
    [arXiv] [Code]

    We present an unsupervised point cloud representation learning method based on global-local bidirectional reasoning, which largely advances the state-of-the-art of unsupervised point cloud understanding and outperforms recent supervised methods.

    dise Spherical Fractal Convolution Neural Networks for Point Cloud Recognition
    Yongming Rao, Jiwen Lu , Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
    [PDF] [Supplement]

    We designed Spherical Fractal Convolution Neural Networks (SFCNN) for rotation-invariant point cloud feature learning.

    dise COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
    Yansong Tang , Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu , Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
    [arXiv] [Project Page] [Annotation Tool]

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

    dise Runtime Network Routing for Efficient Image Classification
    Yongming Rao, Jiwen Lu , Ji Lin , Jie Zhou
    IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI, IF: 17.73), 2019
    [PDF] [Code] [Conference Version (NeurIPS 2017)]

    We propose a generic Runtime Network Routing (RNR) framework for efficient image classification, which selects an optimal path inside the network. Our method can be applied to off-the-shelf neural network structures and easily extended to various application scenarios.

    dise Multi-Proxy Wasserstein Classifier for Image Classification
    Benlin Liu *, Yongming Rao*, Jiwen Lu , Jie Zhou , Cho-Jui Hsieh
    35th AAAI Conference on Artificial Intelligence (AAAI), 2021
    [PDF]

    We present a new Multi-Proxy Wasserstein Classifier to imporve the image classification models by calculating a non-uniform matching flow between the elements in the feature map of a sample and multiple proxies of a class using optimal transport theory.

    dise Temporal Coherence or Temporal Motion: Which is More Critical for Video-based Person Re-identification?
    Guangyi Chen *, Yongming Rao*, Jiwen Lu , Jie Zhou
    16th European Conference on Computer Vision (ECCV), 2020
    [PDF]

    We show temporal coherence plays a more critical role than temporal motion for video-based person re-identification and develop a Adversarial Feature Augmentation (AFA) to highlight temporal coherence.

    dise MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation
    Benlin Liu , Yongming Rao, Jiwen Lu , Jie Zhou , Cho-Jui Hsieh
    16th European Conference on Computer Vision (ECCV), 2020
    [arXiv]

    We boost the performance of CNNs by learning soft targets for shallow layers via meta-learning.

    dise Structure-Preserving Super Resolution with Gradient Guidance
    Cheng Ma , Yongming Rao, Yean Cheng, Ce Chen, Jiwen Lu , Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
    [arXiv] [Code]

    We propose to leverage gradient information as an extra supervision signal to restore structures while generating natural SR images.

    dise Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation
    Cheng Ma , Zhenyu Jiang , Yongming Rao, Jiwen Lu , Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
    [arXiv] [Code]

    We propose a deep face super-resolution method with iterative collaboration between two recurrent networks which focus on facial image recovery and landmark estimation respectively

    dise Learning Discriminative Aggregation Network for Video-based Face Recognition and Person Re-identification
    Yongming Rao, Jiwen Lu , Jie Zhou
    International Journal of Computer Vision (IJCV, IF: 6.07), 2019
    [PDF] [Code]

    We propose a discriminative aggregation network (DAN) method for video-based face recognition and person re-identification, which aims to integrate information from video frames for feature representation effectively and efficiently.

    dise Learning Globally Optimized Object Detector via Policy Gradient
    Yongming Rao, Dahua Lin , Jiwen Lu , Jie Zhou
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018
    Spotlight Presentation
    [PDF] [Supplement]

    We propose a simple yet effective method to learn globally optimized detector for object detection by directly optimizing mAP using the REINFORCE algorithm.

    dise Runtime Neural Pruning
    Ji Lin *, Yongming Rao*, Jiwen Lu , Jie Zhou
    Conference on Neural Information Processing Systems (NeurIPS), 2017
    [PDF] [Code]

    We propose a Runtime Neural Pruning (RNP) framework which prunes the deep neural network dynamically at the runtime.

    dise Learning Discriminative Aggregation Network for Video-Based Face Recognition
    Yongming Rao, Ji Lin , Jiwen Lu , Jie Zhou
    IEEE International Conference on Computer Vision (ICCV), 2017
    Spotlight Presentation
    [PDF] [Code] [Supplement]

    We propose a discriminative aggregation network (DAN) method for video face recognition, which aims to integrate information from video frames effectively and efficiently.

    dise Attention-aware Deep Reinforcement Learning for Video Face Recognition
    Yongming Rao, Jiwen Lu , Jie Zhou
    IEEE International Conference on Computer Vision (ICCV), 2017
    [PDF]

    We propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attentions in face videos for person recognition.

    dise V-tree: Efficient KNN Search on Moving Objects with Road-Network Constraints
    Bilong Shen, Ying Zhao, Guoliang Li, Weimin Zheng, Yue Qin, Bo Yuan, Yongming Rao
    IEEE International Conference on Data Engineering (ICDE), 2017
    [PDF]

    We propose a new tree structure for moving objects kNN search with road-network constraints, which can be used in many real-world applications like taxi search.

    Honors and Awards

  • Baidu Chinese AI New Star Top-100 (百度AI华人新星百强榜)
  • CVPR 2021 Outstanding Reviewer
  • ECCV 2020 Outstanding Reviewer
  • 2nd place in Semi-Supervised Recognition Challenge at FGVC7 (CVPR 2020)
  • 2019 CCF-CV Academic Emerging Award (CCF-CV 学术新锐奖)
  • 2019 National Scholarship, Tsinghua University
  • ICME 2019 Best Reviewer Award
  • 2017 Sensetime Undergraduate Scholarship
  • 1st place in 17th Electronic Design Contest of Tsinghua University
  • 1st place in Momenta Lane Detection Challenge
  • Academic Services

  • Co-organizer: Tutorial on Deep Reinforcement Learning for Computer Vision at CVPR 2019 [website]
  • Conference Reviewer / Program Committee Member: CVPR 2018-2021, ICML 2019-2021, ICCV 2019-2021, NeurIPS 2019-2021, ICLR 2021, ECCV 2020, AAAI 2020-2021, WACV 2020-2021, ACCV 2018-2020, ICME 2019-2021, PRCV 2021, ICPR 2018-2020, ICIP 2018-2019
  • Senior Program Committee Member: IJCAI 2021
  • Journal Reviewer: IJCV, T-IP, T-MM, T-Cybernetics

  • Website Template


    © Yongming Rao | Last updated: June 9, 2021