Yuxiong Wang

Yuxiong WANG



Assistant Professor

Department of Computer Science, University of Illinois at Urbana-Champaign

Email: yxw[at]illinois[dot]edu

Google Scholar


Make things as simple as possible, but not simpler

-- Albert Einstein

Welcome to my homepage. I am an Assistant Professor in the Department of Computer Science at University of Illinois at Urbana-Champaign (UIUC). Before joining Illinois CS, I was a postdoctoral fellow in the Robotics Institute at Carnegie Mellon University, advised by Prof. Martial Hebert. I was a visitor in the Center for Data Science at New York University, working with Prof. Jean Ponce. I obtained my Ph.D. under the supervision of Prof. Martial Hebert in the Robotics Institute. I have also been closely working with Prof. Deva Ramanan and Prof. Ruslan Salakhutdinov. I recently received the Best Paper Honorable Mention Award for streaming perception in ECCV 2020.

My research lies in computer vision, machine learning, and robotics, with a specific focus on meta-learning, few-shot learning, predictive learning, and streaming perception.

I am looking for self-motivated Ph.D. / MS students starting Fall 2022. Please feel free to contact me if interested.

Our group also has openings for interns during Spring / Summer 2022. Please feel free to contact me if interested.

Current Research Topics

  • Meta (reinforcement)-learning and learning to learn
  • Few/low-shot recognition and detection, long-tail recognition
  • Generative modeling, predictive learning
  • Continual learning, transfer learning, domain adaptation
  • Large-scale unsuperivsed, discriminative learning
  • Human motion prediction for human-robot interaction

Group

  • PhD
    • Shengcao Cao
    • Junkun Chen
    • Eric Lee
    • Xiang Li
    • Yunze Man
    • Ziqi Pang
    • Michal Shlapentokh-Rothman
    • Kai Yan
    • Yuanyi Zhong
  • MS
  • Undergraduate

Dissertation

Selected Publications (by date / by topic)

3D Human Motion Prediction and Its Application in Human-Robot Interaction

    Stochastic Dual-Attention Long-Term Motion Prediction

    Learning to Predict Diverse Futures from a Single Past Motion Sequence

    Yuxiong Wang*, Liang-Yan Gui*, José M. F. Moura

    Under review, 2019 (* indicates equal contribution). [Preprint]

    Motion Prediction for Human-Robot Interaction

    Teaching Robots to Predict Human Motion

    Liang-Yan Gui, Kevin Zhang, Yuxiong Wang, Xiaodan Liang, José M. F. Moura, Manuela M. Veloso

    Oral Presentation, IROS, 2018. [PDF]

    Few-Shot Motion Prediction via Meta-Learning

    Few-Shot Human Motion Prediction via Meta-Learning

    Liang-Yan Gui, Yuxiong Wang, Deva Ramanan, José M. F. Moura

    ECCV, 2018. [PDF]

    Human-Like Motion Prediction

    Adversarial Geometry-Aware Human Motion Prediction

    Yuxiong Wang*, Liang-Yan Gui*, Xiaodan Liang, José M. F. Moura

    Oral Presentation, ECCV, 2018 (* indicates equal contribution). [PDF][Talk]

Compositional Learning

    Compositional Learning

    Learning Compositional Representations for Few-Shot Recognition

    Pavel Tokmakov, Yuxiong Wang, Martial Hebert

    Under review, 2019. [PDF]

Knowledge Distillation and Learning to Learn in Model Space

    Few-Shot Motion Prediction via Meta-Learning

    Few-Shot Human Motion Prediction via Meta-Learning

    Liang-Yan Gui, Yuxiong Wang, Deva Ramanan, José M. F. Moura

    ECCV, 2018. [PDF]

    Meta-Learning of Model Dynamics for Long-Tail Recognition
    Learning to Learn through Model Regression Networks

    Learning to Learn: Model Regression Networks for Easy Small Sample Learning

    Yuxiong Wang, Martial Hebert

    ECCV, 2016. [PDF]

Rethinking Fine-Tuning via Developmental Learning

    Continual Learning and Fine-Tuning by Increasing Model Capacity
    Unsupervised Fine-Tuning

    Factorized Convolutional Networks: Unsupervised Fine-Tuning for Image Clustering

    Liang-Yan Gui, Liangke Gui, Yuxiong Wang, Louis-Philippe Morency, José M. F. Moura

    Oral Presentation, WACV, 2018. [PDF]

Unsupervised Meta-Learning

    Improving CNN Transferability through Large-Scale Unsupervised Meta-Training

    Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs

    Yuxiong Wang, Martial Hebert

    NeurIPS, 2016. [PDF]

    Unsupervised Binary Codes

    Few-Shot Hash Learning for Image Retrieval

    Yuxiong Wang, Liangke Gui, Martial Hebert

    ICCV Workshops, 2017. [PDF]

    Discovering Unsupervised Binary Codes for Learning from Small Sample Sets

    Yuxiong Wang, Martial Hebert

    CVPR BigVision Workshop, 2016.

    Transfer Learning from Unsupervised Universal Sources

    Learning by Transferring from Unsupervised Universal Sources

    Yuxiong Wang, Martial Hebert

    Oral Presentation, AAAI, 2016. [PDF]

    Transfer Learning from Unsupervised Universal Sources

    Model Recommendation: Generating Object Detectors from Few Samples

    Yuxiong Wang, Martial Hebert

    CVPR, 2015. [PDF][Project]

Non-Negative Matrix and Tensor Factorization and Its Applications

    Non-Negative Matrix Factorization: A Comprehensive Review

    Non-Negative Matrix Factorization: A Comprehensive Review

    Yuxiong Wang, Yu-Jin Zhang

    IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp.1336-1353, 2013. [PDF]

    Non-negative Matrix Factorization: A Comprehensive Review

    Image Inpainting via Weighted Sparse Non-Negative Matrix Factorization

    Yuxiong Wang, Yu-Jin Zhang

    ICIP, 2011. [PDF]

    Neighborhood Preserving Non-Negative Tensor Factorization for Image Representation

    Yuxiong Wang, Liangyan Gui, Yu-Jin Zhang

    Oral Presentation, ICASSP, 2012. [PDF]

    Non-Negative Matrix Factorization for Image Representation

    Yuxiong Wang

    Best Master’s Thesis, Tsinghua University, May 2012.

Sparse Representation and Dictionary Learning for Image Classification

    Self-Explanatory Sparse Representation for Image Classification

    Self-Explanatory Sparse Representation for Image Classification

    Yuxiong Wang*, Baodi Liu*, Bin Shen, Yu-Jin Zhang, Martial Hebert

    ECCV, 2014 (* indicates equal contribution). [PDF][Poster]

    Learning Dictionary on Manifolds for Image Classification

    Learning Dictionary on Manifolds for Image Classification

    Bao-Di Liu, Yuxiong Wang, Yu-Jin Zhang, Bin Shen

    Pattern Recognition, vol. 46, no. 7, pp. 1879-1890, 2013. [PDF]

    Blockwise Coordinate Descent Schemes for Sparse Representation

    Bao-Di Liu, Yuxiong Wang, Bin Shen, Yu-Jin Zhang, Yanjiang Wang

    ICASSP, 2014. [PDF]

    Discriminant Sparse Coding for Image Classification

    Baodi Liu, Yuxiong Wang, Yu-Jin Zhang, Yin Zheng

    ICASSP, 2012. [PDF]