World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8439
World Ranking
10530
National Ranking
4414

Xuming He 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 Xuming He 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: 213 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Xuming He 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 Xuming He 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Xuming He is affiliated with Washington University in St. Louis in the United States. Their research primarily spans the fields of Computer Science and Engineering, with significant contributions in subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Statistics and Probability, and Control and Systems Engineering.

The scientist has focused on several main topics within their research, including Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, Advanced Image and Video Retrieval Techniques, Advanced Neural Network Applications, Human Pose and Action Recognition, Neural Networks and Reservoir Computing, and Optical Network Technologies.

Recent papers authored or co-authored by Xuming He cover diverse areas across computer vision, AI, and computational methods. These include:

  • SGTR: End-to-end Scene Graph Generation with Transformer, 2022, published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Learning Cross-Modal Context Graph for Visual Grounding, 2020, published in the Proceedings of the AAAI Conference on Artificial Intelligence
  • CALIP: Zero-Shot Enhancement of CLIP with Parameter-Free Attention, 2023, published in the Proceedings of the AAAI Conference on Artificial Intelligence
  • DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation, 2021, published in Nature Communications
  • Deep photonic reservoir computing recurrent network, 2023, published in Optica

Xuming He frequently collaborates with several co-authors, including Songyang Zhang, Chuyu Zhang, Jingyi Yu, Rongjie Li, and Cheng Wang. These collaborations have contributed to multiple publications spanning various topics within AI and engineering.

The scientist's work appears regularly in well-regarded publication venues. The most frequent outlets include arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), APL Machine Learning, and the 2022 IEEE Power & Energy Society General Meeting (PESGM).

Collectively, Xuming He's research integrates methods in artificial intelligence, deep learning, and multimodal applications with a particular emphasis on advancing techniques in computer vision and neural network architectures.

Best Publications

  • Multiscale conditional random fields for image labeling

    Xuming He;R.S. Zemel;M.A. Carreira-Perpinan

  • DER: Dynamically Expandable Representation for Class Incremental Learning

    Shipeng Yan;Jiangwei Xie;Xuming He

  • Learning and Incorporating Top-Down Cues in Image Segmentation

    Xuming He;Richard S. Zemel;Debajyoti Ray

  • Discrete-Continuous Depth Estimation from a Single Image

    Miaomiao Liu;Mathieu Salzmann;Xuming He

  • Part-aware Prototype Network for Few-shot Semantic Segmentation

    Yongfei Liu;Xiangyi Zhang;Songyang Zhang;Xuming He

  • Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

    Shuailin Li;Chuyu Zhang;Xuming He

  • Distribution Alignment: A Unified Framework for Long-tail Visual Recognition

    Songyang Zhang;Zeming Li;Shipeng Yan;Xuming He

  • Learning and incorporating top-down cues in image segmentation

    Xuming He;Richard S. Zemel;Debajyoti Ray

  • Forest Change Detection in Incomplete Satellite Images With Deep Neural Networks

    Salman H. Khan;Xuming He;Fatih Porikli;Mohammed Bennamoun

  • Pose-Aware Multi-Level Feature Network for Human Object Interaction Detection

    Bo Wan;Desen Zhou;Yongfei Liu;Rongjie Li

  • Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation

    Rongjie Li;Songyang Zhang;Bo Wan;Xuming He

  • SentiCap: generating image descriptions with sentiments

    Alexander Mathews;Lexing Xie;Xuming He

  • Boundary-Aware Instance Segmentation

    Zeeshan Hayder;Xuming He;Mathieu Salzmann

  • Indoor scene structure analysis for single image depth estimation

    Wei Zhuo;Mathieu Salzmann;Xuming He;Miaomiao Liu

  • SemStyle: Learning to Generate Stylised Image Captions Using Unaligned Text

    Alexander Mathews;Lexing Xie;Xuming He

  • SGTR: End-to-end Scene Graph Generation with Transformer

    Unknown

  • Multiclass semantic video segmentation with object-level active inference

    Buyu Liu;Xuming He

  • Dynamic Context Correspondence Network for Semantic Alignment

    Shuaiyi Huang;Qiuyue Wang;Songyang Zhang;Shipeng Yan

  • CALIP: Zero-Shot Enhancement of CLIP with Parameter-Free Attention

    Unknown

  • Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition

    Tailin Chen;Desen Zhou;Jian Wang;Shidong Wang

  • Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation

    Heng Yang;Xuming He;Xuhui Jia;Ioannis Patras

  • Geometry-Aware Deep Network for Single-Image Novel View Synthesis

    Miaomiao Liu;Xuming He;Mathieu Salzmann

  • Shape-Aware Semi-supervised 3D Semantic Segmentation for Medical Images

    Shuailin Li;Chuyu Zhang;Xuming He

  • Learning Cross-Modal Context Graph for Visual Grounding

    Yongfei Liu;Bo Wan;Xiaodan Zhu;Xuming He

  • LatentGNN: Learning Efficient Non-local Relations for Visual Recognition

    Songyang Zhang;Shipeng Yan;Xuming He

Frequent Co-Authors

Nick Barnes
Nick Barnes Australian National University
Mathieu Salzmann
Mathieu Salzmann École Polytechnique Fédérale de Lausanne
Fatih Porikli
Fatih Porikli Australian National University
Lexing Xie
Lexing Xie Australian National University
Elinor McKone
Elinor McKone Australian National University
Hongdong Li
Hongdong Li Australian National University
Richard S. Zemel
Richard S. Zemel University of Toronto
Stephen Gould
Stephen Gould Australian National University
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Mohammed Bennamoun
Mohammed Bennamoun University of Western Australia

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