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Zezhou Cheng

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I am a tenure-track assistant professor in the Department of Computer Science at the University of Virginia, leading the Computer Vision Lab. Before joining UVA, I was a Postdoctoral Researcher at Caltech, advised by Georgia Gkioxari. I obtained my Ph.D. in Computer Science at UMass Amherst in 2023, where I was co-advised by Subhransu Maji and Daniel Sheldon. I received my Bachelor's degree at Sichuan University in China in 2015. During my undergraduate studies, I worked with Qingxiong Yang and Bin Sheng at Shanghai Jiao Tong University. 

My research interests include: 

  • Label-efficient visual understanding (self-/semi/weakly-supervised learning)

  • 3D shape understanding, generation, and manipulation. 

  • Applications of computer vision in other domains (e.g., ecology, chemistry, etc.)

 Please visit Computer Vision Lab @ UVa  for more information about our research, teaching, lab members, etc. 

Publications  [Google Scholar]

Point-MoE: Towards Cross-Domain Generalization in 3D Semantic Segmentation via Mixture-of-Experts 

Xuweiyi Chen, Wentao Zhou, Aruni RoyChowdhury, Zezhou Cheng

arXiv:2505.23926

[PDF | Project Website] 

Keywords:  3D understanding 

Frame In-N-Out: Unbounded Controllable Image-to-Video Generation 

Boyang Wang, Xuweiyi Chen, Matheus Gadelha, Zezhou Cheng

arXiv 2505.21491

[PDF | Project Website | Source code (coming soon)] 

Keywords:  Controllable video generation 

Probing the Mid-level Vision Capabilities of Self-Supervised Learning 

Xuweiyi Chen, Markus Marks, Zezhou Cheng

Computer Vision and Pattern Recognition (CVPR), 2025.

[PDF | Project Website | Source code] 

Keywords:  Self-supervised learning, 3D representation learning 

SAB3R: Semantic-Augmented Backbone in 3D Reconstruction 

Xuweiyi Chen, Tian Xia, Sihan Xu, Jed Jianing Yang, Joyce Chai, Zezhou Cheng

3D-LLM/VLA Workshop @ CVPR 2025

[PDF | Project Website] 

Keywords:  3D understanding 

Learning 3D Representations from Procedural 3D Programs 

Xuweiyi Chen, Zezhou Cheng

arXiv:2411.17467

[PDF | Project Website | Dataset | Source code] 

Keywords:  Self-supervised learning, 3D representation learning 

Open Vocabulary Monocular 3D Object Detection 

Jin Yao, Hao Gu, Xuweiyi Chen, Jiayun Wang, Zezhou Cheng

arXiv:2411.16833

[PDF | Project Website | Source code] 

Keywords: 3D recognition 

Before 2024 Fall 

Using spatio-temporal information in weather radar data to detect and track communal bird roosts 

Gustavo Perez, Wenlong Zhao, Zezhou Cheng, Maria Carolina Belotti, Yuting Deng, Victoria Simons, Elske Tielens, Jeffrey Kelly, Kyle Horton, Subhransu Maji, Daniel Sheldon

Remote Sensing in Ecology and Conservation. 2024; 

[PDF | Source code] 

Keywords: AI for ecology

LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs 

Zezhou Cheng, Carlos Esteves, Varun Jampani, Abhishek Kar, Subhransu Maji, Ameesh Makadia

IEEE International Conference on Computer Vision (ICCV), 2023.

[PDF | Project Website] 

Keywords: 3D pose estimation, Neural rendering

Accidental Turntables: Learning 3D Pose by Watching Objects Turn 

Zezhou Cheng,  Matheus Gadelha, Subhransu Maji

ICCV workshop on Recovering 6D Object Pose (ICCV workshop), 2023.

[PDF | Project Website | Poster] 

Keywords: unsupervised learning; 3D pose estimation.

ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites 

Yachan Liu*, Gustavo Perez*, Zezhou Cheng, Aaron Sun, Samuel Hoover, Wei Fan, Subhransu Maji, Peng Bai (*equal contribution)

Journal of Materials Chemistry A, 2023.

[PDF | Project Website] 

Keywords: 3D shape understanding; AI for Chemistry 

A Semi-Automated System to Annotate Communal Roosts in Large-Scale Weather Radar Data 

Wenlong Zhao, Gustavo Perez, Zezhou Cheng, Maria Carolina Belotti, Yuting Deng, Victoria Simons, Elske Tielens, Jeffrey Kelly, Kyle Horton, Subhransu Maji, Daniel Sheldon

NeurIPS 2023 Computational Sustainability: Pitfalls and Promises from Theory to Deployment

[PDF | Project Website] 

Keywords: AI for ecology 

Cross-Modal 3D Shape Generation and Manipulation 

Zezhou Cheng, Menglei Chai, Jian Ren, Hsin-Ying Lee, Kyle Olszewski, Zeng Huang, Subhransu Maji, Sergey Tulyakov

European Conference on Computer Vision (ECCV) 2022

[PDF | Source code | Project Website | Poster | Video] 

Keywords: 3D shape generation and manipulation

Long-term analysis of persistence and size of swallow and martin roosts in the US Great Lakes 

Maria Belotti, Yuting Deng, Wenlong Zhao, Victoria Simons, Zezhou Cheng, Gustavo Perez, Elske Tielens, Subhransu Maji, Daniel Sheldon, Jeffrey Kelly, Kyle Horton

Remote Sensing in Ecology and Conservation, 2022

[PDF] 

Keywords: AI for ecology

Quantifying Long-term Phenological Patterns of Aerial Insectivores Roosting in the Great Lakes Region using Weather Surveillance Radar 

Yuting Deng, Maria Belotti, Wenlong Zhao, Zezhou Cheng, Gustavo Perez, Elske Tielens, Victoria Simons, Daniel Sheldon, Subhransu Maji, Jeff Kelly, Kyle Horton

Global Change Biology, 2022 (impact factor: 13.21)

[PDF] 

Keywords: AI for ecology

Improving Few-Shot Part Segmentation using Coarse Supervision 

Oindrila Saha, Zezhou Cheng, Subhransu Maji

European Conference on Computer Vision (ECCV) 2022

[PDF | Source code | Project Website] 

Keywords: Self-/Semi-/Weakly-supervised learning

GANORCON: Are Generative Models Useful for Few-shot Segmentation? 

Oindrila Saha, Zezhou Cheng, Subhransu Maji

Computer Vision and Pattern Recognition (CVPR), 2022.

[PDF | Source code | Project Website]

Keywords: Self-/Semi-/Weakly-supervised learning

On Equivariant and Invariant Learning of Object Landmark Representations 

Zezhou Cheng,  Jong-Chyi Su, Subhransu Maji

IEEE International Conference on Computer Vision (ICCV), 2021.

[PDF | Source code | Project Website | arXiv  | Poster  | Supplementary]

Keywords: Self-/Semi-/Weakly-supervised learning

AI for conservation: learning to track birds with radar

Zezhou Cheng, Subhransu Maji, Daniel Sheldon

 ACM XRDS 2021

[PDF] 

Keywords: AI for ecology

A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

 Jong-Chyi Su, Zezhou Cheng, Subhransu Maji

Computer Vision and Pattern Recognition (CVPR), 2021, (Oral).

[PDF | Source code | arXiv | Poster]

Keywords: Self-/Semi-/Weakly-supervised learning

Detecting and Tracking Communal Bird Roosts in Weather Radar Data

Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler

 Association for the Advancement of Artificial Intelligence (AAAI), 2020, (Oral)

[PDF | Source code | Project Website | arXiv | Supplementary] 

Keywords: AI for ecology

A Bayesian Perspective on the Deep Image Prior

Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon 

Computer Vision and Pattern Recognition (CVPR), 2019.

[PDF | Source code | Project Website | arXiv | Poster | Supplementary ]

Keywords: Image restoration; Self-/Semi-/Weakly-supervised learning

Colorization Using Neural Network Ensemble 

Zezhou Cheng, Qingxiong Yang, and Bin Sheng 

IEEE Transactions on Image Processing (TIP), 2017.

[PDF | Souce code | Project Website]

Keywords: Image restoration

Deep Colorization

Zezhou Cheng, Qingxiong Yang, and Bin Sheng 

IEEE International Conference on Computer Vision (ICCV), 2015. 

[PDF | Souce code | Colorized Images (view online)] (My undergraduate thesis)

Keywords: Image restoration

Industry Experience

  • Summer  2022:  Research Intern at Google Research (NYC). 

    • Hosts: Carlos Esteves, Ameesh Makadia

    • Collaborators:  Varun Jampani, Abhishek Kar

  • Summer  2021:  Research Intern at Snap Research (Remote). 

    • Hosts: Menglei Chai, Sergey Tulyakov

    • Collaborators: Jian Ren, Hsin-Ying Lee, Kyle Olszewski, Zeng Huan

  • Summer  2019:  Research Intern at Amazon (Pasadena, CA.) 

    • Host: Vijay Mahadevan

Services

Reviewer:

Conferences: NeurIPS/ ICCV / CVPR / AAAI / WACV / ICLR / ICML etc.

Journals: IJCV / TIP / TMM, etc.

Teaching

Teaching Assistant: 

Computer Vision CICS 670.  Fall 2022, UMass Amherst (link)

Lecturer: 

Caltech AI Bootcamp, Spring 2024. (link) 

Awards

Travel Award from Doctoral Consortium, CVPR 2023 

Outstanding Reviewer, CVPR 2021

Outstanding Synthesis Award, UMass Amherst CICS, 2020 (top 2 synthesis) 

AAAI-2020 Student Scholarship  2020

Best Poster Award at the New England Computer Vision Workshop held at Brown University  2019

UMass Amherst CICS Edward Riseman and Allen Hanson Scholarship  2018

National Scholarship, Ministry of Education, China  2016 (at Shanghai Jiao Tong University)

National Scholarship, Ministry of Education, China  2014 (at Sichuan University)

Media Coverage

Climate Change AI: Deep learning of nanoporous materials for chemical separations

UMass CICS Outstanding Synthesis: CICS Graduate Students Honored for Outstanding Synthesis Projects


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