World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

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

Zhe Gan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Zhe Gan sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 560 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 153 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Zhe Gan D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Zhe Gan sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 67 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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)

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