World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
48
Citations
8705
World Ranking
364
National Ranking
121

Computer Science

D-Index
43
Citations
8665
World Ranking
7928
National Ranking
1041

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Zuxuan Wu is affiliated with Fudan University in China and has contributed extensively to the field of computer science, particularly in areas related to computer vision and artificial intelligence. Their research spans several subfields and topics within computer science, reflecting a broad and multidisciplinary approach to advanced machine learning applications.

The primary fields of study in Wu's work include:

  • Computer Science

Within this main field, the subfields of study are:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Signal Processing
  • Control and Systems Engineering
  • Neurology

Wu's research topics cover a variety of machine learning and computer vision challenges, including:

  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Adversarial Robustness in Machine Learning
  • Generative Adversarial Networks and Image Synthesis
  • Video Analysis and Summarization

Zuxuan Wu has published research in several high-profile venues frequently used in the computer vision and artificial intelligence communities. The most common publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Multimedia
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

The scientist has also been involved in book publication through Springer International Publishing with the title:

  • Deep Learning for Video Understanding (2024)

Wu's recent papers demonstrate involvement in topics such as domain adaptation, video transformers, image manipulation detection, 3D object detection, and adversarial attacks on vision transformers. Representative recent papers include:

  • Cross-Domain Contrastive Learning for Unsupervised Domain Adaptation, 2022, IEEE Transactions on Multimedia
  • BEVT: BERT Pretraining of Video Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • ObjectFormer for Image Manipulation Detection and Localization, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers, 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Towards Transferable Adversarial Attacks on Vision Transformers, 2022, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent coauthors contributing alongside Wu include:

  • Yu-Gang Jiang
  • Jingjing Chen
  • Junke Wang
  • Larry S. Davis
  • Zejia Weng

Best Publications

  • VITON: An Image-Based Virtual Try-on Network

    Xintong Han;Zuxuan Wu;Zhe Wu;Ruichi Yu

  • Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

    Zuxuan Wu;Xi Wang;Yu-Gang Jiang;Hao Ye

  • Learning Fashion Compatibility with Bidirectional LSTMs

    Xintong Han;Zuxuan Wu;Yu-Gang Jiang;Larry S. Davis

  • BlockDrop: Dynamic Inference Paths in Residual Networks

    Zuxuan Wu;Tushar Nagarajan;Abhishek Kumar;Steven Rennie

  • Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks

    Yu-Gang Jiang;Zuxuan Wu;Jun Wang;Xiangyang Xue

  • M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection.

    Junke Wang;Zuxuan Wu;Jingjing Chen;Yu-Gang Jiang

  • DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation

    Zuxuan Wu;Xintong Han;Yen-Liang Lin;Mustafa Gökhan Uzunbas

  • Cross-Domain Contrastive Learning for Unsupervised Domain Adaptation

    Unknown

  • Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

    Zuxuan Wu;Ser-Nam Lim;Larry S. Davis;Tom Goldstein

  • AdaFrame: Adaptive Frame Selection for Fast Video Recognition

    Zuxuan Wu;Caiming Xiong;Chih-Yao Ma;Richard Socher

  • Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification

    Zuxuan Wu;Yu-Gang Jiang;Xi Wang;Hao Ye

  • Automatic Spatially-Aware Fashion Concept Discovery

    Xintong Han;Zuxuan Wu;Phoenix X. Huang;Xiao Zhang

  • BEVT: BERT Pretraining of Video Transformers.

    Rui Wang;Dongdong Chen;Zuxuan Wu;Yinpeng Chen

  • Self-Monitoring Navigation Agent via Auxiliary Progress Estimation

    Chih-Yao Ma;Jiasen Lu;Zuxuan Wu;Ghassan AlRegib

  • The Regretful Agent: Heuristic-Aided Navigation Through Progress Estimation

    Chih-Yao Ma;Zuxuan Wu;Ghassan AlRegib;Caiming Xiong

  • Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification

    Zuxuan Wu;Yu-Gang Jiang;Jun Wang;Jian Pu

  • Deep learning for video classification and captioning

    Zuxuan Wu;Ting Yao;Yanwei Fu;Yu-Gang Jiang

  • OmniVL: One Foundation Model for Image-Language and Video-Language Tasks

    Unknown

  • Evaluating Two-Stream CNN for Video Classification

    Hao Ye;Zuxuan Wu;Rui-Wei Zhao;Xi Wang

  • M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers.

    Tianrui Guan;Jun Wang;Shiyi Lan;Rohan Chandra

  • Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification

    Yu-Gang Jiang;Zuxuan Wu;Jinhui Tang;Zechao Li

  • Harnessing Object and Scene Semantics for Large-Scale Video Understanding

    Zuxuan Wu;Yanwei Fu;Yu-Gang Jiang;Leonid Sigal

  • Self-Monitoring Navigation Agent via Auxiliary Progress Estimation

    Chih-Yao Ma;Jiasen Lu;Zuxuan Wu;Ghassan AlRegib

  • FLAG: Adversarial Data Augmentation for Graph Neural Networks

    Kezhi Kong;Guohao Li;Mucong Ding;Zuxuan Wu

Frequent Co-Authors

Yu-Gang Jiang
Yu-Gang Jiang Fudan University
Larry S. Davis
Larry S. Davis University of Maryland, College Park
Tom Goldstein
Tom Goldstein University of Maryland, College Park
Caiming Xiong
Caiming Xiong Salesforce (United States)
Xiangyang Xue
Xiangyang Xue Fudan University
Shih-Fu Chang
Shih-Fu Chang Columbia University
Abhinav Shrivastava
Abhinav Shrivastava University of Maryland, College Park
Serge Belongie
Serge Belongie University of Copenhagen
Claire Cardie
Claire Cardie Cornell University

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