World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
77
Citations
47731
World Ranking
1231
National Ranking
650

Zhuowen Tu 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 Zhuowen Tu 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: 250 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Zhuowen Tu 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 Zhuowen Tu 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: 77 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Overview

Zhuowen Tu is affiliated with the University of California, San Diego in the United States. Their research activity is concentrated within the field of Computer Science, with a substantial focus on subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Computer Graphics and Computer-Aided Design, and Aerospace Engineering.

The scientist's work encompasses a range of topics such as Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, Advanced Image and Video Retrieval Techniques, Generative Adversarial Networks and Image Synthesis, Topic Modeling, and Natural Language Processing Techniques.

Zhuowen Tu has contributed to numerous scientific publications, with frequent appearances in the following venues:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Recent selected papers authored or co-authored by Zhuowen Tu include:

  • MeMOT: Multi-Object Tracking with Memory, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Text Spotting Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • ViTGAN: Training GANs with Vision Transformers, 2021, arXiv (Cornell University)
  • BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions, 2024, Proceedings of the AAAI Conference on Artificial Intelligence
  • Instance Segmentation with Mask-supervised Polygonal Boundary Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

The scientist has worked extensively with several frequent co-authors, including:

  • Stefano Soatto
  • Tyler A. Chang
  • Yuanjun Xiong
  • Weijian Xu
  • Vijay Mahadevan

Best Publications

  • Aggregated Residual Transformations for Deep Neural Networks

    Saining Xie;Ross Girshick;Piotr Dollar;Zhuowen Tu

  • Holistically-Nested Edge Detection

    Saining Xie;Zhuowen Tu

  • Similarity network fusion for aggregating data types on a genomic scale

    Bo Wang;Aziz M Mezlini;Feyyaz Demir;Marc Fiume

  • Integral Channel Features

    Piotr Dollár;Zhuowen Tu;Pietro Perona;Serge J. Belongie

  • Deeply-Supervised Nets

    Chen-Yu Lee;Saining Xie;Patrick W. Gallagher;Zhengyou Zhang

  • Deeply Supervised Salient Object Detection with Short Connections

    Qibin Hou;Ming-Ming Cheng;Xiaowei Hu;Ali Borji

  • Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

    Saining Xie;Chen Sun;Jonathan Huang;Zhuowen Tu

  • Deeply Supervised Salient Object Detection with Short Connections

    Qibin Hou;Ming-Ming Cheng;Xiaowei Hu;Ali Borji

  • Image parsing : Unifying segmentation, detection, and recognition

    Zhuowen Tu;Xiangrong Chen;Alan L. Yuille;Song Chun Zhu

  • Detecting texts of arbitrary orientations in natural images

    Cong Yao;Xiang Bai;Wenyu Liu;Yi Ma

  • Image segmentation by data-driven Markov chain Monte Carlo

    Zhuowen Tu;Song-Chun Zhu

  • Image parsing: unifying segmentation, detection, and recognition

    Zhuowen Tu;Xiangrong Chen;Yuille;Zhu

  • Holistically-Nested Edge Detection

    Saining Xie;Zhuowen Tu

  • Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

    Zhuowen Tu;Xiang Bai

  • Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods

    J. E. Iglesias;Cheng-Yi Liu;P. M. Thompson;Zhuowen Tu

  • Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering

    Zhuowen Tu

  • Supervised Learning of Edges and Object Boundaries

    P. Dollar;Zhuowen Tu;S. Belongie

  • Robust Point Matching via Vector Field Consensus

    Jiayi Ma;Ji Zhao;Jinwen Tian;Alan L. Yuille

  • Deeply-Supervised Nets

    Chen-Yu Lee;Saining Xie;Patrick Gallagher;Zhengyou Zhang

  • Cluster-Based Co-Saliency Detection

    Huazhu Fu;Xiaochun Cao;Zhuowen Tu

  • Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

    Chen-Yu Lee;Patrick W. Gallagher;Zhuowen Tu

Frequent Co-Authors

Xiang Bai
Xiang Bai Huazhong University of Science and Technology
Arthur W. Toga
Arthur W. Toga University of Southern California
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Paul M. Thompson
Paul M. Thompson University of Southern California
Wenyu Liu
Wenyu Liu Huazhong University of Science and Technology
Eric Chang
Eric Chang Microsoft (United States)
Xinggang Wang
Xinggang Wang Huazhong University of Science and Technology
Song-Chun Zhu
Song-Chun Zhu Peking University
Georg Langs
Georg Langs Medical University of Vienna
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University

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