World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
26361
World Ranking
2528
National Ranking
43

Hanwang Zhang 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 Hanwang Zhang 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: 195 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

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

Hanwang Zhang 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 Hanwang Zhang 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: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Overview

Hanwang Zhang is affiliated with Nanyang Technological University in Singapore. Their research primarily focuses on computer science, with a significant emphasis on areas such as artificial intelligence and computer vision and pattern recognition.

The scientist has contributed extensively to domain adaptation and few-shot learning, as well as multimodal machine learning applications. Other main topics covered in their work include topic modeling, advanced image and video retrieval techniques, advanced neural network applications, natural language processing techniques, and human pose and action recognition.

Frequent co-authors in their research include Qianru Sun, Xian-Sheng Hua, Beier Zhu, Xuanyu Yi, and Kaihua Tang.

Their publishing record shows a strong presence in several venues, notably:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Transactions on Multimedia
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Recent papers illustrating their research activities are:

  • Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect, 2020, arXiv (Cornell University)
  • Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Deconfounded Image Captioning: A Causal Retrospect, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Therapeutic Role of Mesenchymal Stem Cell-Derived Extracellular Vesicles in Female Reproductive Diseases, 2021, Frontiers in Endocrinology
  • Interventional Few-Shot Learning, 2020, arXiv (Cornell University)

Best Publications

  • Neural Collaborative Filtering

    Xiangnan He;Lizi Liao;Hanwang Zhang;Liqiang Nie

  • SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

    Long Chen;Hanwang Zhang;Jun Xiao;Liqiang Nie

  • Fast Matrix Factorization for Online Recommendation with Implicit Feedback

    Xiangnan He;Hanwang Zhang;Min-Yen Kan;Tat-Seng Chua

  • Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

    Jun Xiao;Hao Ye;Xiangnan He;Hanwang Zhang

  • Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

    Jingyuan Chen;Hanwang Zhang;Xiangnan He;Liqiang Nie

  • Auto-Encoding Scene Graphs for Image Captioning

    Xu Yang;Kaihua Tang;Hanwang Zhang;Jianfei Cai

  • Unbiased Scene Graph Generation From Biased Training

    Kaihua Tang;Yulei Niu;Jianqiang Huang;Jiaxin Shi

  • Video Captioning With Attention-Based LSTM and Semantic Consistency

    Lianli Gao;Zhao Guo;Hanwang Zhang;Xing Xu

  • Visual Translation Embedding Network for Visual Relation Detection

    Hanwang Zhang;Zawlin Kyaw;Shih-Fu Chang;Tat-Seng Chua

  • Learning to Compose Dynamic Tree Structures for Visual Contexts

    Kaihua Tang;Hanwang Zhang;Baoyuan Wu;Wenhan Luo

  • Attributed Social Network Embedding

    Lizi Liao;Xiangnan He;Hanwang Zhang;Tat-Seng Chua

  • Counterfactual VQA: A Cause-Effect Look at Language Bias

    Yulei Niu;Kaihua Tang;Hanwang Zhang;Zhiwu Lu

  • Video Question Answering via Gradually Refined Attention over Appearance and Motion

    Dejing Xu;Zhou Zhao;Jun Xiao;Fei Wu

  • Counterfactual Samples Synthesizing for Robust Visual Question Answering

    Long Chen;Xin Yan;Jun Xiao;Hanwang Zhang

  • Zero-Shot Visual Recognition Using Semantics-Preserving Adversarial Embedding Networks

    Long Chen;Hanwang Zhang;Jun Xiao;Wei Liu

  • Discrete Collaborative Filtering

    Hanwang Zhang;Fumin Shen;Wei Liu;Xiangnan He

  • Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect

    Kaihua Tang;Jianqiang Huang;Hanwang Zhang

  • Causal intervention for weakly-supervised semantic segmentation

    Dong Zhang;Hanwang Zhang;Jinhui Tang;Xian-Sheng Hua

  • Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder

    Jingkuan Song;Hanwang Zhang;Xiangpeng Li;Lianli Gao

  • Learning to Assemble Neural Module Tree Networks for Visual Grounding

    Daqing Liu;Hanwang Zhang;Zheng-Jun Zha;Feng Wu

  • Visual Commonsense R-CNN

    Tan Wang;Jianqiang Huang;Hanwang Zhang;Qianru Sun

  • Grounding Referring Expressions in Images by Variational Context

    Hanwang Zhang;Yulei Niu;Shih-Fu Chang

Frequent Co-Authors

Tat-Seng Chua
Tat-Seng Chua National University of Singapore
Xiangnan He
Xiangnan He University of Science and Technology of China
Zheng-Jun Zha
Zheng-Jun Zha University of Science and Technology of China
Shih-Fu Chang
Shih-Fu Chang Columbia University
Yang Yang
Yang Yang University of Electronic Science and Technology of China
Jun Xiao
Jun Xiao University of Wisconsin–Madison
Meng Wang
Meng Wang Hefei University of Technology
Wei Liu
Wei Liu Tencent (China)
Liqiang Nie
Liqiang Nie Shandong University
Qianru Sun
Qianru Sun Singapore Management University

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