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Yuxiong WANG Assistant Professor Department of Computer Science, University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign Faculty Fellow, National Center for Supercomputing Applications (NCSA) Email: yxw[at]illinois[dot]edu |
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-- 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 receive the Best Paper Honorable Mention Award for streaming perception in ECCV 2020, and Best Paper Award Finalist in CVPR 2019, 2022. I am recognized as a Notable Area Chair in ICLR 2023, and an Expert Reviewer in TMLR.
My research lies in computer vision, machine learning, and robotics, with a specific focus on open-world AI, meta-learning, few-shot learning, predictive learning, and streaming perception.
I am looking for self-motivated Ph.D. / MS students starting Fall 2023. Please feel free to contact me if interested.
Our group also has openings for interns during Spring / Summer 2023. Please feel free to contact me if interested.
Current Research Topics
Group
Dissertation
Selected Publications
2023
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study
Junkun Chen*, Haoran Tang*, Yuanyi Zhong*, Yu-Xiong Wang
MV-Map: Offboard HD-Map Generation with Multi-view Consistency
Ziyang Xie*, Ziqi Pang*, Yu-Xiong Wang
Improving Equivariance in State-of-the-Art Supervised Depth and Normal Predictors
Yuanyi Zhong, Anand Bhattad, Yu-Xiong Wang, David A. Forsyth
Video State-Changing Object Segmentation
Jiangwei Yu*, Xiang Li*, Xinran Zhao, Hongming Zhang, Yu-Xiong Wang
Multi-task View Synthesis with Neural Radiance Fields
Shuhong Zheng, Zhipeng Bao, Martial Hebert, Yu-Xiong Wang
InterDiff: Forecasting 3D Human-Object Interaction with Physics-Informed Diffusion
Sirui Xu, Zhengyuan Li, Yu-Xiong Wang, Liangyan Gui
Revisiting Deformable Convolution for Depth Completion
Xinglong Sun, Jean Ponce, Yu-Xiong Wang
Streaming Motion Forecasting for Autonomous Driving
Ziqi Pang, Deva Ramanan, Mengtian Li, Yu-Xiong Wang
DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
Yunze Man, Liangyan Gui, Yu-Xiong Wang
Learning Lightweight Object Detectors via Progressive Knowledge Distillation
Shengcao Cao, Mengtian Li, James Hays, Deva Ramanan, Yu-Xiong Wang, Liangyan Gui
NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds
Jun-Kun Chen, Jipeng Lyu, Yu-Xiong Wang
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
Ziqi Pang, Jie Li, Pavel Tokmakov, Dian Chen, Sergey Zagoruyko, Yu-Xiong Wang
Contrastive Mean Teacher for Domain Adaptive Object Detectors
Shengcao Cao, Dhiraj Joshi, Liangyan Gui, Yu-Xiong Wang
BEV-Guided Multi-Modality Fusion for Driving Perception
Yunze Man, Liangyan Gui, Yu-Xiong Wang
Object Discovery from Motion-Guided Tokens
Zhipeng Bao, Pavel Tokmakov, Yu-Xiong Wang, Adrien Gaidon, Martial Hebert
GAPS: Few-Shot Incremental Semantic Segmentation via Guided Copy-Paste Synthesis
Ri-Zhao Qiu, Peiyi Chen, Wangzhe Sun, Yu-Xiong Wang, Kris Hauser
CVPR Workshop on Learning with Limited Labelled Data, 2023. [PDF] [Website]
From N to N+1: Learning to Detect Novel Animals with SAM in the Wild
Garvita Allabadi, Ana Lucic, Yu-Xiong Wang, Vikram Adve
Stochastic Multi-Person 3D Motion Forecasting
Sirui Xu, Yu-Xiong Wang*, Liangyan Gui*
Do Pre-Trained Models Benefit Equally in Continual Learning?
Kuan-Ying Lee, Yuanyi Zhong, Yu-Xiong Wang
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study
Junkun Chen*, Haoran Tang*, Yuanyi Zhong*, Yu-Xiong Wang
MV-Map: Offboard HD-Map Generation with Multi-view Consistency
Ziyang Xie*, Ziqi Pang*, Yu-Xiong Wang
Improving Equivariance in State-of-the-Art Supervised Depth and Normal Predictors
Yuanyi Zhong, Anand Bhattad, Yu-Xiong Wang, David A. Forsyth
Video State-Changing Object Segmentation
Jiangwei Yu*, Xiang Li*, Xinran Zhao, Hongming Zhang, Yu-Xiong Wang
Multi-task View Synthesis with Neural Radiance Fields
Shuhong Zheng, Zhipeng Bao, Martial Hebert, Yu-Xiong Wang
InterDiff: Forecasting 3D Human-Object Interaction with Physics-Informed Diffusion
Sirui Xu, Zhengyuan Li, Yu-Xiong Wang, Liangyan Gui
Revisiting Deformable Convolution for Depth Completion
Xinglong Sun, Jean Ponce, Yu-Xiong Wang
Streaming Motion Forecasting for Autonomous Driving
Ziqi Pang, Deva Ramanan, Mengtian Li, Yu-Xiong Wang
DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
Yunze Man, Liangyan Gui, Yu-Xiong Wang
Learning Lightweight Object Detectors via Progressive Knowledge Distillation
Shengcao Cao, Mengtian Li, James Hays, Deva Ramanan, Yu-Xiong Wang, Liangyan Gui
NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds
Jun-Kun Chen, Jipeng Lyu, Yu-Xiong Wang
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
Ziqi Pang, Jie Li, Pavel Tokmakov, Dian Chen, Sergey Zagoruyko, Yu-Xiong Wang
Contrastive Mean Teacher for Domain Adaptive Object Detectors
Shengcao Cao, Dhiraj Joshi, Liangyan Gui, Yu-Xiong Wang
BEV-Guided Multi-Modality Fusion for Driving Perception
Yunze Man, Liangyan Gui, Yu-Xiong Wang
Object Discovery from Motion-Guided Tokens
Zhipeng Bao, Pavel Tokmakov, Yu-Xiong Wang, Adrien Gaidon, Martial Hebert
GAPS: Few-Shot Incremental Semantic Segmentation via Guided Copy-Paste Synthesis
Ri-Zhao Qiu, Peiyi Chen, Wangzhe Sun, Yu-Xiong Wang, Kris Hauser
CVPR Workshop on Learning with Limited Labelled Data, 2023. [PDF] [Website]
From N to N+1: Learning to Detect Novel Animals with SAM in the Wild
Garvita Allabadi, Ana Lucic, Yu-Xiong Wang, Vikram Adve
Stochastic Multi-Person 3D Motion Forecasting
Sirui Xu, Yu-Xiong Wang*, Liangyan Gui*
Do Pre-Trained Models Benefit Equally in Continual Learning?
Kuan-Ying Lee, Yuanyi Zhong, Yu-Xiong Wang
2022
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations
Kai Yan, Alexander G. Schwing, Yu-Xiong Wang
LECO: Learning with an Evolving Class Ontology
Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan, Shu Kong
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence
Brando Miranda, Patrick Yu, Yu-Xiong Wang, Oluwasanmi Koyejo
NeurIPS Workshop on Meta-Learning, Contributed Talk, 2022. [PDF] [Poster]
Towards Overcoming Data Scarcity in Materials Science: Unifying Models and Datasets with a Mixture of Experts Framework
Rees Chang, Yu-Xiong Wang, Elif Ertekin
Generalized Few-Shot Node Classification on Graphs
Zhe Xu, Kaize Ding, Yu-Xiong Wang, Huan Liu, Hanghang Tong
ICDM, 2022. [PDF]
Diverse Human Motion Prediction Guided by Multi-Level Spatial Temporal Anchors
Sirui Xu, Yu-Xiong Wang*, Liang-Yan Gui*
PointTree: Transformation Robust Point Cloud Encoder with Relaxed K-D Trees
Junkun Chen, Yu-Xiong Wang
Generative Modeling for Multi-Task Visual Learning
Zhipeng Bao, Martial Hebert, Yu-Xiong Wang
Is Self-Supervised Contrastive Learning More Robust Than Supervised Learning?
Yuanyi Zhong, Haoran Tang, Junkun Chen, Jian Peng, Yu-Xiong Wang
ICML Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022. [PDF]
DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering
Liwen Wu, Jae Yong Lee, Anand Bhattad, Yu-Xiong Wang, David Forsyth
Discovering Objects that Can Move
Zhipeng Bao, Pavel Tokmakov, Allan Jabri, Yu-Xiong Wang, Adrien Gaidon, Martial Hebert
Long-Tailed Recognition via Weight Balancing
Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, Shu Kong
Embracing Single Stride 3D Object Detector with Sparse Transformer
Lue Fan, Ziqi Pang, Tianyuan Zhang, Yu-Xiong Wang, Hang Zhao, Feng Wang, Naiyan Wang, Zhaoxiang Zhang
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations
Kai Yan, Alexander G. Schwing, Yu-Xiong Wang
LECO: Learning with an Evolving Class Ontology
Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan, Shu Kong
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence
Brando Miranda, Patrick Yu, Yu-Xiong Wang, Oluwasanmi Koyejo
NeurIPS Workshop on Meta-Learning, Contributed Talk, 2022. [PDF] [Poster]
Towards Overcoming Data Scarcity in Materials Science: Unifying Models and Datasets with a Mixture of Experts Framework
Rees Chang, Yu-Xiong Wang, Elif Ertekin
Generalized Few-Shot Node Classification on Graphs
Zhe Xu, Kaize Ding, Yu-Xiong Wang, Huan Liu, Hanghang Tong
ICDM, 2022. [PDF]
Diverse Human Motion Prediction Guided by Multi-Level Spatial Temporal Anchors
Sirui Xu, Yu-Xiong Wang*, Liang-Yan Gui*
PointTree: Transformation Robust Point Cloud Encoder with Relaxed K-D Trees
Junkun Chen, Yu-Xiong Wang
Generative Modeling for Multi-Task Visual Learning
Zhipeng Bao, Martial Hebert, Yu-Xiong Wang
Is Self-Supervised Contrastive Learning More Robust Than Supervised Learning?
Yuanyi Zhong, Haoran Tang, Junkun Chen, Jian Peng, Yu-Xiong Wang
ICML Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022. [PDF]
DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering
Liwen Wu, Jae Yong Lee, Anand Bhattad, Yu-Xiong Wang, David Forsyth
Discovering Objects that Can Move
Zhipeng Bao, Pavel Tokmakov, Allan Jabri, Yu-Xiong Wang, Adrien Gaidon, Martial Hebert
Long-Tailed Recognition via Weight Balancing
Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, Shu Kong
Embracing Single Stride 3D Object Detector with Sparse Transformer
Lue Fan, Ziqi Pang, Tianyuan Zhang, Yu-Xiong Wang, Hang Zhao, Feng Wang, Naiyan Wang, Zhaoxiang Zhang
2021
SIRfyN: Single Image Relighting from your Neighbors
David Forsyth, Anand Bhattad, Pranav Asthana, Yuanyi Zhong, Yu-Xiong Wang
Arxiv, 2021. [PDF]
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, Yuxiong Wang
ICCV, 2021. [PDF]
Learning to Hallucinate Examples from Extrinsic and Intrinsic Supervision
Liangke Gui*, Adrien Bardes*, Ruslan Salakhutdinov, Alexander Hauptmann, Martial Hebert, Yuxiong Wang
ICCV, 2021 (* indicates equal contribution). [PDF]
On the Importance of Distractors for Few-Shot Learning
Rajshekhar Das, Yuxiong Wang, José M. F. Moura
ICCV, 2021. [PDF]
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
Nadine Chang, Zhiding Yu, Yuxiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez
ICML, 2021. [PDF]
Hallucination Improves Few-Shot Object Detection
Weilin Zhang, Yuxiong Wang
CVPR, 2021. [PDF]
DAP: Detection-Aware Pre-training with Weak Supervision
Yuanyi Zhong, Jianfeng Wang, Lijuan Wang, Jian Peng, Yuxiong Wang, , Lei Zhang
CVPR, 2021. [PDF]
Unlocking the Full Potential of Small Data with Diverse Supervision
Ziqi Pang, Zhiyuan Hu, Pavel Tokmakov, Yuxiong Wang, Martial Hebert
CVPR Workshop on Learning from Limited or Imperfect Data, 2021. [PDF]
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
Zhipeng Bao, Yuxiong Wang, Martial Hebert
ICLR, 2021. [PDF]
SIRfyN: Single Image Relighting from your Neighbors
David Forsyth, Anand Bhattad, Pranav Asthana, Yuanyi Zhong, Yu-Xiong Wang
Arxiv, 2021. [PDF]
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, Yuxiong Wang
ICCV, 2021. [PDF]
Learning to Hallucinate Examples from Extrinsic and Intrinsic Supervision
Liangke Gui*, Adrien Bardes*, Ruslan Salakhutdinov, Alexander Hauptmann, Martial Hebert, Yuxiong Wang
ICCV, 2021 (* indicates equal contribution). [PDF]
On the Importance of Distractors for Few-Shot Learning
Rajshekhar Das, Yuxiong Wang, José M. F. Moura
ICCV, 2021. [PDF]
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
Nadine Chang, Zhiding Yu, Yuxiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez
ICML, 2021. [PDF]
Hallucination Improves Few-Shot Object Detection
Weilin Zhang, Yuxiong Wang
CVPR, 2021. [PDF]
DAP: Detection-Aware Pre-training with Weak Supervision
Yuanyi Zhong, Jianfeng Wang, Lijuan Wang, Jian Peng, Yuxiong Wang, , Lei Zhang
CVPR, 2021. [PDF]
Unlocking the Full Potential of Small Data with Diverse Supervision
Ziqi Pang, Zhiyuan Hu, Pavel Tokmakov, Yuxiong Wang, Martial Hebert
CVPR Workshop on Learning from Limited or Imperfect Data, 2021. [PDF]
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
Zhipeng Bao, Yuxiong Wang, Martial Hebert
ICLR, 2021. [PDF]
2020
Towards Streaming Perception
Mengtian Li, Yuxiong Wang, Deva Ramanan
Oral Presentation, Best Paper Honorable Mention, ECCV, 2020. [PDF] [Project]
Alpha Net:
Adaptation with Composition in Classifier Space
Nadine Chang, Jayanth Koushik, Michael J. Tarr, Martial Hebert, Yuxiong Wang
Under review, 2020. [Preprint]
Progressive Knowledge Distillation for Few-Shot Learning
Yuxiong Wang*, Adrien Bardes*, Ruslan Salakhutdinov, Martial Hebert
Under review, 2020 (* indicates equal contribution). [Preprint]
Also in [NeurIPS Workshop on Learning with Rich Experience], 2019. [Poster]
Few-Shot Generative Adversarial Networks
Chunyan Bai, Yan Xu, Boyu Liu, Ruslan Salakhutdinov, Martial Hebert, Yuxiong Wang
Under review, 2020. [Preprint]
Prototypical Adaptation for Few-Shot Classification
Rajshekhar Das, Yuxiong Wang, José M. F. Moura
Under review, 2020. [Preprint]
Learning Generalizable Representations via Diverse Supervision
Ziqi Pang, Zhiyuan Hu, Pavel Tokmakov, Yuxiong Wang, Martial Hebert
Under review, 2020. [arXiv]
Learning to Predict Diverse Futures from a Single Past Motion Sequence
Yuxiong Wang*, Liang-Yan Gui*, José M. F. Moura
Under review, 2020. [Preprint]
Meta-Learning by Hallucinating Useful Examples
Yuxiong Wang*, Yuki Uchiyama*, Martial Hebert, Karteek Alahari
Under review, 2020. [Preprint]
Towards Streaming Perception
Mengtian Li, Yuxiong Wang, Deva Ramanan
Oral Presentation, Best Paper Honorable Mention, ECCV, 2020. [PDF] [Project]
Alpha Net: Adaptation with Composition in Classifier Space
Nadine Chang, Jayanth Koushik, Michael J. Tarr, Martial Hebert, Yuxiong Wang
Under review, 2020. [Preprint]
Progressive Knowledge Distillation for Few-Shot Learning
Yuxiong Wang*, Adrien Bardes*, Ruslan Salakhutdinov, Martial Hebert
Under review, 2020 (* indicates equal contribution). [Preprint]
Also in [NeurIPS Workshop on Learning with Rich Experience], 2019. [Poster]
Few-Shot Generative Adversarial Networks
Chunyan Bai, Yan Xu, Boyu Liu, Ruslan Salakhutdinov, Martial Hebert, Yuxiong Wang
Under review, 2020. [Preprint]
Prototypical Adaptation for Few-Shot Classification
Rajshekhar Das, Yuxiong Wang, José M. F. Moura
Under review, 2020. [Preprint]
Learning Generalizable Representations via Diverse Supervision
Ziqi Pang, Zhiyuan Hu, Pavel Tokmakov, Yuxiong Wang, Martial Hebert
Under review, 2020. [arXiv]
Learning to Predict Diverse Futures from a Single Past Motion Sequence
Yuxiong Wang*, Liang-Yan Gui*, José M. F. Moura
Under review, 2020. [Preprint]
Meta-Learning by Hallucinating Useful Examples
Yuxiong Wang*, Yuki Uchiyama*, Martial Hebert, Karteek Alahari
Under review, 2020. [Preprint]
2019
Meta-Learning to Detect Rare Objects
Yuxiong Wang, Deva Ramanan, Martial Hebert,
Learning Compositional Representations for Few-Shot Recognition
Pavel Tokmakov, Yuxiong Wang, Martial Hebert
Image Deformation Meta-Networks for One-Shot Learning
Zitian Chen, Yanwei Fu, Yuxiong Wang, Lin Ma, Wei Liu, Martial Hebert
Oral Presentation, Best Paper Award Finalist, CVPR, 2019. [PDF][Poster][Talk]
Embodied One-Shot Video Recognition: Learning from Actions of a Virtual Embodied Agent
Yuqian Fu, Chengrong Wang, Yanwei Fu, Yuxiong Wang, Cong Bai, Xiangyang Xue, Yu-Gang
Jiang, Lin Ma, Wei Liu, Martial Hebert
ACM MM, 2019. [PDF]
Meta-Learning to Detect Rare Objects
Yuxiong Wang, Deva Ramanan, Martial Hebert,
Learning Compositional Representations for Few-Shot Recognition
Pavel Tokmakov, Yuxiong Wang, Martial Hebert
Image Deformation Meta-Networks for One-Shot Learning
Zitian Chen, Yanwei Fu, Yuxiong Wang, Lin Ma, Wei Liu, Martial Hebert
Oral Presentation, Best Paper Award Finalist, CVPR, 2019. [PDF][Poster][Talk]
Embodied One-Shot Video Recognition: Learning from Actions of a Virtual Embodied Agent
Yuqian Fu, Chengrong Wang, Yanwei Fu, Yuxiong Wang, Cong Bai, Xiangyang Xue, Yu-Gang
Jiang, Lin Ma, Wei Liu, Martial Hebert
ACM MM, 2019. [PDF]
2018
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 Human Motion Prediction via Meta-Learning
Liang-Yan Gui, Yuxiong Wang, Deva Ramanan, José M. F. Moura
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][Poster][Talk]
Low-Shot Learning from Imaginary Data
Yuxiong Wang, Ross Girshick, Martial Hebert,
Bharath Hariharan
Spotlight Oral Presentation,
CVPR, 2018. [PDF][Poster][Talk]
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]
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 Human Motion Prediction via Meta-Learning
Liang-Yan Gui, Yuxiong Wang, Deva Ramanan, José M. F. Moura
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][Poster][Talk]
Low-Shot Learning from Imaginary Data
Yuxiong Wang, Ross Girshick, Martial Hebert, Bharath Hariharan
Spotlight Oral Presentation, CVPR, 2018. [PDF][Poster][Talk]
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]
2017
Learning to Model the Tail
Yuxiong Wang, Deva Ramanan, Martial Hebert
Also in [Contributed Talk, NeurIPS Workshop on Meta-Learning], 2017.
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.
[Poster]
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Yuxiong Wang, Deva Ramanan, Martial Hebert
Also in [Invited Talk, CVPR Workshop on Continuous and Open-Set Learning], 2017.
Learning to Model the Tail
Yuxiong Wang, Deva Ramanan, Martial Hebert
Also in [Contributed Talk, NeurIPS Workshop on Meta-Learning], 2017.
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. [Poster]
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Yuxiong Wang, Deva Ramanan, Martial Hebert
Also in [Invited Talk, CVPR Workshop on Continuous and Open-Set Learning], 2017.
2016
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs
Yuxiong Wang, Martial Hebert
Learning to Learn: Model Regression Networks for Easy Small Sample Learning
Yuxiong Wang, Martial Hebert
Learning by Transferring from Unsupervised Universal Sources
Yuxiong Wang, Martial Hebert
Oral Presentation,
AAAI, 2016. [PDF]
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs
Yuxiong Wang, Martial Hebert
Learning to Learn: Model Regression Networks for Easy Small Sample Learning
Yuxiong Wang, Martial Hebert
Learning by Transferring from Unsupervised Universal Sources
Yuxiong Wang, Martial Hebert
Oral Presentation, AAAI, 2016. [PDF]
2015
Model Recommendation: Generating Object Detectors from Few Samples
Yuxiong Wang, Martial Hebert
Model Recommendation: Generating Object Detectors from Few Samples
Yuxiong Wang, Martial Hebert
2014
Self-Explanatory Sparse Representation for Image Classification
Yuxiong Wang*, Baodi Liu*, Bin Shen, Yu-Jin Zhang, Martial Hebert
Blockwise Coordinate Descent Schemes for Sparse Representation
Bao-Di Liu, Yuxiong Wang, Bin Shen, Yu-Jin Zhang, Yanjiang Wang
ICASSP, 2014. [PDF]
Self-Explanatory Sparse Representation for Image Classification
Yuxiong Wang*, Baodi Liu*, Bin Shen, Yu-Jin Zhang, Martial Hebert
Blockwise Coordinate Descent Schemes for Sparse Representation
Bao-Di Liu, Yuxiong Wang, Bin Shen, Yu-Jin Zhang, Yanjiang Wang
ICASSP, 2014. [PDF]
2013
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]
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]
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]
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]
2012
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.
Discriminant Sparse Coding for Image Classification
Baodi Liu, Yuxiong Wang, Yu-Jin Zhang, Yin Zheng
ICASS, 2012. [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.
Discriminant Sparse Coding for Image Classification
Baodi Liu, Yuxiong Wang, Yu-Jin Zhang, Yin Zheng
ICASS, 2012. [PDF]
2011
