World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
71785
World Ranking
2973
National Ranking
404

Gao Huang 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 Gao Huang 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: 249 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: 559 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: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 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: 313 publications — 76th percentile

76% of scientists in this discipline score the same or lower.

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

Gao Huang 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 Gao Huang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 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: 61 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Overview

Gao Huang is affiliated with Tsinghua University in China and has made significant contributions in the field of computer science, with a particular focus on computer vision and pattern recognition. Their research spans several subfields including artificial intelligence, organic chemistry, radiology, nuclear medicine and imaging, and electrical and electronic engineering.

The scientist's publication record includes a broad range of topics such as domain adaptation and few-shot learning, advanced neural network applications, multimodal machine learning applications, advanced image and video retrieval techniques, human pose and action recognition, reinforcement learning in robotics, and machine learning and data classification.

Frequent coauthors in their work include Shiji Song, Yulin Wang, Yizeng Han, and Haojun Jiang. Gao Huang's research has been consistently published in prominent venues including arXiv (Cornell University), IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, Proceedings of the AAAI Conference on Artificial Intelligence, and the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Recent publications exemplify Gao Huang's research focus and impact. These include:

  • "Vision Transformer with Deformable Attention," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Dynamic Neural Networks: A Survey," 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "On the Integration of Self-Attention and Convolution," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study," 2021, The Lancet Digital Health
  • "SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation," 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence

Best Publications

  • Densely Connected Convolutional Networks

    Gao Huang;Zhuang Liu;Laurens van der Maaten;Kilian Q. Weinberger

  • Learning Efficient Convolutional Networks through Network Slimming

    Zhuang Liu;Jianguo Li;Zhiqiang Shen;Gao Huang

  • Trends in extreme learning machines

    Gao Huang;Guang-Bin Huang;Shiji Song;Keyou You

  • Deep Networks with Stochastic Depth

    Gao Huang;Yu Sun;Zhuang Liu;Daniel Sedra

  • Densely Connected Convolutional Networks

    Gao Huang;Zhuang Liu;Laurens van der Maaten;Kilian Q. Weinberger

  • Rethinking the Value of Network Pruning

    Zhuang Liu;Mingjie Sun;Tinghui Zhou;Gao Huang

  • CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

    Gao Huang;Shichen Liu;Laurens van der Maaten;Kilian Q. Weinberger

  • Semi-Supervised and Unsupervised Extreme Learning Machines

    Gao Huang;Shiji Song;Jatinder N. D. Gupta;Cheng Wu

  • Vision Transformer with Deformable Attention

    Unknown

  • Dynamic Neural Networks: A Survey

    Yizeng Han;Gao Huang;Shiji Song;Le Yang

  • Snapshot Ensembles: Train 1, Get M for Free

    Gao Huang;Yixuan Li;Geoff Pleiss;Zhuang Liu

  • Convolutional Networks with Dense Connectivity

    Gao Huang;Zhuang Liu;Geoff Pleiss;Laurens van der Maaten

  • Multi-Scale Dense Networks for Resource Efficient Image Classification

    Gao Huang;Danlu Chen;Tianhong Li;Felix Wu

  • On the Integration of Self-Attention and Convolution

    Unknown

  • Horizontal Pyramid Matching for Person Re-Identification

    Yang Fu;Yunchao Wei;Yuqian Zhou;Honghui Shi

  • 3D Object Detection with Pointformer

    Xuran Pan;Zhuofan Xia;Shiji Song;Li Erran Li

  • Rethinking the Value of Network Pruning

    Zhuang Liu;Mingjie Sun;Tinghui Zhou;Gao Huang

  • Deep Networks with Stochastic Depth

    Gao Huang;Yu Sun;Zhuang Liu;Daniel Sedra

  • BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision

    Unknown

  • Resource Aware Person Re-identification Across Multiple Resolutions

    Yan Wang;Lequn Wang;Yurong You;Xu Zou

  • Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation

    Shuang Li;Shiji Song;Gao Huang;Zhengming Ding

  • An empirical study on evaluation metrics of generative adversarial networks

    Qiantong Xu;Gao Huang;Yang Yuan;Chuan Guo

  • Resolution Adaptive Networks for Efficient Inference

    Le Yang;Yizeng Han;Xi Chen;Shiji Song

  • Supervised word mover's distance

    Gao Huang;Chuan Quo;Matt J. Kusner;Yu Sun

  • Anytime Stereo Image Depth Estimation on Mobile Devices

    Yan Wang;Zihang Lai;Gao Huang;Brian H. Wang

  • Implicit Semantic Data Augmentation for Deep Networks

    Yulin Wang;Xuran Pan;Shiji Song;Hong Zhang

Frequent Co-Authors

Shiji Song
Shiji Song Tsinghua University
Kilian Q. Weinberger
Kilian Q. Weinberger Cornell University
Cheng Wu
Cheng Wu Tsinghua University
Laurens van der Maaten
Laurens van der Maaten Facebook (United States)
Chi Harold Liu
Chi Harold Liu Beijing Institute of Technology
Jifeng Dai
Jifeng Dai Tsinghua University
Zhengming Ding
Zhengming Ding Tulane University
Stephen Lin
Stephen Lin Microsoft Research Asia (China)
Yanwei Fu
Yanwei Fu Fudan University
John E. Hopcroft
John E. Hopcroft Cornell University

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