World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
67
Citations
18092
World Ranking
2195
National Ranking
1101

Overview

Zhe Gan is affiliated with Microsoft in the United States and has contributed extensively to the field of computer science. Their research primarily focuses on areas such as computer vision, artificial intelligence, and multimodal machine learning applications. The subfields in which they have published include computer vision and pattern recognition, artificial intelligence, cancer research, signal processing, and language and linguistics.

The main research topics covered by Zhe Gan's work include:

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

Zhe Gan has published numerous papers in various notable venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Foundations and Trends® in Computer Graphics and Vision
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Selected recent publications by Zhe Gan and close collaborators are:

  • An Empirical Study of Training End-to-End Vision-and-Language Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Large-Scale Adversarial Training for Vision-and-Language Representation Learning, 2020, arXiv (Cornell University)
  • SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA, 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • GIT: A Generative Image-to-text Transformer for Vision and Language, 2022, arXiv (Cornell University)

Their collaborative work includes frequent partnerships with these researchers:

  • Lijuan Wang
  • Zicheng Liu
  • Linjie Li
  • Jingjing Liu
  • Shuohang Wang

Best Publications

  • AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

    Tao Xu;Pengchuan Zhang;Qiuyuan Huang;Han Zhang

  • UNITER: UNiversal Image-TExt Representation Learning

    Yen-Chun Chen;Linjie Li;Licheng Yu;Ahmed El Kholy

  • Variational autoencoder for deep learning of images, labels and captions

    Yunchen Pu;Zhe Gan;Ricardo Henao;Xin Yuan

  • Patient Knowledge Distillation for BERT Model Compression

    Siqi Sun;Yu Cheng;Zhe Gan;Jingjing Liu

  • Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling

    Jie Lei;Linjie Li;Luowei Zhou;Zhe Gan

  • An Empirical Study of Training End-to-End Vision-and-Language Transformers

    Unknown

  • Semantic Compositional Networks for Visual Captioning

    Zhe Gan;Chuang Gan;Xiaodong He;Yunchen Pu

  • GIT: A Generative Image-to-text Transformer for Vision and Language

    Unknown

  • HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

    Linjie Li;Yen-Chun Chen;Yu Cheng;Zhe Gan

  • Relation-Aware Graph Attention Network for Visual Question Answering

    Linjie Li;Zhe Gan;Yu Cheng;Jingjing Liu

  • FreeLB: Enhanced Adversarial Training for Natural Language Understanding

    Chen Zhu;Yu Cheng;Zhe Gan;Siqi Sun

  • UNITER: Learning UNiversal Image-TExt Representations

    Yen-Chun Chen;Linjie Li;Licheng Yu;Ahmed El Kholy

  • Scaling Up Vision-Language Pretraining for Image Captioning

    Unknown

  • Large-Scale Adversarial Training for Vision-and-Language Representation Learning

    Zhe Gan;Yen-Chun Chen;Linjie Li;Chen Zhu

  • StyleNet: Generating Attractive Visual Captions with Styles

    Chuang Gan;Zhe Gan;Xiaodong He;Jianfeng Gao

  • Adversarial feature matching for text generation

    Yizhe Zhang;Zhe Gan;Kai Fan;Zhi Chen

  • Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization

    Yizhe Zhang;Michel Galley;Jianfeng Gao;Zhe Gan

  • Discourse-Aware Neural Extractive Text Summarization

    Jiacheng Xu;Zhe Gan;Yu Cheng;Jingjing Liu

  • SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning

    Kevin Lin;Linjie Li;Chung-Ching Lin;Faisal Ahmed

  • An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

    Zhengyuan Yang;Zhe Gan;Jianfeng Wang;Xiaowei Hu

  • StoryGAN: A Sequential Conditional GAN for Story Visualization

    Yitong Li;Zhe Gan;Yelong Shen;Jingjing Liu

  • Hierarchical Graph Network for Multi-hop Question Answering

    Yuwei Fang;Siqi Sun;Zhe Gan;Rohit Pillai

  • Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation

    Liyiming Ke;Xiujun Li;Yonatan Bisk;Ari Holtzman

Frequent Co-Authors

Yu Cheng
Yu Cheng Microsoft (United States)
Lawrence Carin
Lawrence Carin Duke University
Chunyuan Li
Chunyuan Li Microsoft (United States)
Liqun Chen
Liqun Chen University of Surrey
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Guoyin Wang
Guoyin Wang Chongqing University of Posts and Telecommunications
Xiaodong He
Xiaodong He Chinese Academy of Sciences
Zhangyang Wang
Zhangyang Wang The University of Texas at Austin
Zicheng Liu
Zicheng Liu Microsoft (United States)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science in the USA opens doors to a range of related degrees and career opportunities—many of which are now available online. For those interested in environmental impacts and sustainability, environmental engineering degrees online blend technical knowledge with real-world impact, making them a popular option.

Cost is an important factor for many students. The mechanical engineering cost of education varies, but affordable online programs offer flexibility without sacrificing quality.

If you are curious about the physical sciences, it's now possible to get a physics degree online. These programs provide foundational knowledge and skills that can support diverse tech careers.

For those aiming for high-growth industries, data science degrees build expertise in one of today's most in-demand fields. Exploring these online options can expand your professional pathways while meeting your personal and financial needs.

Best Scientists Citing Zhe Gan

Trending Scientists

Recently Published Articles