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Rising Stars
2025

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Rising Stars

D-Index
58
Citations
14780
World Ranking
179
National Ranking
22

Computer Science

D-Index
58
Citations
14155
World Ranking
3616
National Ranking
1733

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Chuang Gan is a researcher affiliated with the University of Massachusetts Amherst in the United States. Their primary field of study is Computer Science, with a focus on several subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Control and Systems Engineering, and Computational Mechanics.

Their recent scholarly contributions include papers such as MCUNet: Tiny Deep Learning on IoT Devices (2020, arXiv [Cornell University]), TransCenter: Transformers With Dense Representations for Multiple-Object Tracking (2022, IEEE Transactions on Pattern Analysis and Machine Intelligence), and Location-Aware Graph Convolutional Networks for Video Question Answering (2020, Proceedings of the AAAI Conference on Artificial Intelligence). Other notable works include ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation (2020, arXiv [Cornell University]) and RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning (2021, Proceedings of the AAAI Conference on Artificial Intelligence).

Their frequent coauthors reflect collaborations across a range of experts in the field, with notable names including:

  • Joshua B. Tenenbaum
  • Peihao Chen
  • Mingkui Tan
  • Yikang Shen
  • Antonio Torralba

Chuang Gan's scholarly work has appeared extensively in specific publication venues. These include:

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

The main research topics explored by Chuang Gan encompass a broad range of areas within computer science and artificial intelligence. These topics include:

  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Domain Adaptation and Few-Shot Learning
  • Reinforcement Learning in Robotics
  • Topic Modeling
  • Advanced Image and Video Retrieval Techniques
  • Advanced Neural Network Applications

Through their publications and collaborations, Chuang Gan has contributed to advancing knowledge in multiple aspects of machine learning, computer vision, and related computational techniques, emphasizing both theoretical frameworks and practical applications.

Best Publications

  • TSM: Temporal Shift Module for Efficient Video Understanding

    Ji Lin;Chuang Gan;Song Han

  • Once for All: Train One Network and Specialize it for Efficient Deployment

    Han Cai;Chuang Gan;Tianzhe Wang;Zhekai Zhang

  • Graph Convolutional Networks for Temporal Action Localization

    Runhao Zeng;Wenbing Huang;Chuang Gan;Mingkui Tan

  • The Sound of Pixels

    Hang Zhao;Chuang Gan;Chuang Gan;Andrew Rouditchenko;Carl Vondrick;Carl Vondrick

  • Semantic Compositional Networks for Visual Captioning

    Zhe Gan;Chuang Gan;Xiaodong He;Yunchen Pu

  • The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

    Jiayuan Mao;Chuang Gan;Pushmeet Kohli;Joshua B. Tenenbaum

  • Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

    Kexin Yi;Jiajun Wu;Chuang Gan;Antonio Torralba

  • DevNet: A Deep Event Network for multimedia event detection and evidence recounting

    Chuang Gan;Naiyan Wang;Yi Yang;Dit-Yan Yeung

  • StyleNet: Generating Attractive Visual Captions with Styles

    Chuang Gan;Zhe Gan;Xiaodong He;Jianfeng Gao

  • Dense Regression Network for Video Grounding

    Runhao Zeng;Haoming Xu;Wenbing Huang;Peihao Chen

  • MCUNet: Tiny Deep Learning on IoT Devices

    Ji Lin;Wei-Ming Chen;Yujun Lin;john cohn

  • The Sound of Motions

    Hang Zhao;Chuang Gan;Wei-Chiu Ma;Antonio Torralba

  • Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering

    Xiangpeng Li;Jingkuan Song;Lianli Gao;Xianglong Liu

  • End-to-End Learning of Motion Representation for Video Understanding

    Lijie Fan;Wenbing Huang;Chuang Gan;Stefano Ermon

  • Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification

    Xiang Long;Chuang Gan;Gerard de Melo;Jiajun Wu

  • CLEVRER: Collision Events for Video Representation and Reasoning

    Kexin Yi;Chuang Gan;Yunzhu Li;Pushmeet Kohli

  • Recurrent Topic-Transition GAN for Visual Paragraph Generation

    Xiaodan Liang;Zhiting Hu;Hao Zhang;Chuang Gan

  • HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

    Hanrui Wang;Zhanghao Wu;Zhijian Liu;Han Cai

  • Learning Attributes Equals Multi-Source Domain Generalization

    Chuang Gan;Tianbao Yang;Boqing Gong

  • TransCenter: Transformers With Dense Representations for Multiple-Object Tracking

    Unknown

  • TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning

    Han Cai;Chuang Gan;Ligeng Zhu;Song Han

  • Graph Convolutional Networks for Temporal Action Localization

    Runhao Zeng;Wenbing Huang;Mingkui Tan;Yu Rong

Frequent Co-Authors

Jiajun Wu
Jiajun Wu Stanford University
Wenbing Huang
Wenbing Huang Renmin University of China
Mingkui Tan
Mingkui Tan South China University of Technology
Boqing Gong
Boqing Gong Google (United States)
Junzhou Huang
Junzhou Huang The University of Texas at Arlington
Sijia Liu
Sijia Liu Michigan State University
Alexander G. Hauptmann
Alexander G. Hauptmann Carnegie Mellon University

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