World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
70
Citations
45826
World Ranking
1819
National Ranking
924

Bolei Zhou 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 Bolei Zhou 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: 161 publications — 31st percentile

31% of scientists in this discipline score the same or lower.

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

Bolei Zhou 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 Bolei Zhou 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: 70 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Overview

Bolei Zhou is affiliated with the University of California, Los Angeles in the United States. Their research primarily focuses on areas within Computer Science and Engineering, with a significant emphasis on Computer Vision and Pattern Recognition and Artificial Intelligence.

The scientist has contributed extensively to research topics including:

  • Generative Adversarial Networks and Image Synthesis
  • Advanced Vision and Imaging
  • Autonomous Vehicle Technology and Safety
  • Reinforcement Learning in Robotics
  • Human Pose and Action Recognition
  • Advanced Image and Video Retrieval Techniques
  • Advanced Neural Network Applications

Bolei Zhou has frequently published in several prominent venues, notably:

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

Some of their recent papers include:

  • Understanding the role of individual units in a deep neural network, 2020, Proceedings of the National Academy of Sciences
  • Cross-View Semantic Segmentation for Sensing Surroundings, 2020, IEEE Robotics and Automation Letters
  • TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis, 2021, International Journal of Computer Vision
  • MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence

Their research collaborations include frequent co-authors such as Yinghao Xu, Yujun Shen, Ceyuan Yang, Zhenghao Peng, and Quanyi Li.

Best Publications

  • Learning Deep Features for Discriminative Localization

    Bolei Zhou;Aditya Khosla;Agata Lapedriza;Aude Oliva

  • Places: A 10 Million Image Database for Scene Recognition

    Bolei Zhou;Agata Lapedriza;Aditya Khosla;Aude Oliva

  • Learning Deep Features for Scene Recognition using Places Database

    Bolei Zhou;Agata Lapedriza;Jianxiong Xiao;Antonio Torralba

  • Scene Parsing through ADE20K Dataset

    Bolei Zhou;Hang Zhao;Xavier Puig;Sanja Fidler

  • Unified Perceptual Parsing for Scene Understanding

    Tete Xiao;Yingcheng Liu;Bolei Zhou;Yuning Jiang

  • Semantic Understanding of Scenes Through the ADE20K Dataset

    Bolei Zhou;Hang Zhao;Xavier Puig;Tete Xiao

  • Network Dissection: Quantifying Interpretability of Deep Visual Representations

    David Bau;Bolei Zhou;Aditya Khosla;Aude Oliva

  • Object Detectors Emerge in Deep Scene CNNs

    Bolei Zhou;Aditya Khosla;Agata Lapedriza;Aude Oliva

  • Temporal Relational Reasoning in Videos

    Bolei Zhou;Alex Andonian;Aude Oliva;Antonio Torralba

  • Interpreting the Latent Space of GANs for Semantic Face Editing

    Yujun Shen;Jinjin Gu;Xiaoou Tang;Bolei Zhou

  • Measuring human perceptions of a large-scale urban region using machine learning

    Fan Zhang;Fan Zhang;Fan Zhang;Bolei Zhou;Liu Liu;Yu Liu

  • InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs.

    Yujun Shen;Ceyuan Yang;Xiaoou Tang;Bolei Zhou

  • Scene Graph Generation from Objects, Phrases and Region Captions

    Yikang Li;Wanli Ouyang;Bolei Zhou;Kun Wang

  • Closed-Form Factorization of Latent Semantics in GANs

    Yujun Shen;Bolei Zhou

  • Moments in Time Dataset: One Million Videos for Event Understanding

    Mathew Monfort;Carl Vondrick;Aude Oliva;Alex Andonian

  • In-Domain GAN Inversion for Real Image Editing

    Jiapeng Zhu;Yujun Shen;Deli Zhao;Bolei Zhou

  • Person Search with Natural Language Description

    Shuang Li;Tong Xiao;Hongsheng Li;Bolei Zhou

  • Temporal Pyramid Network for Action Recognition

    Ceyuan Yang;Yinghao Xu;Jianping Shi;Bo Dai

  • Places: An Image Database for Deep Scene Understanding

    Bolei Zhou;Aditya Khosla;Àgata Lapedriza;Antonio Torralba

  • Semantic photo manipulation with a generative image prior

    David Bau;Hendrik Strobelt;William Peebles;Jonas Wulff

  • GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

    David Bau;Jun-Yan Zhu;Hendrik Strobelt;Bolei Zhou

  • Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents

    Bolei Zhou;Xiaogang Wang;Xiaoou Tang

  • Simple Baseline for Visual Question Answering

    Bolei Zhou;Yuandong Tian;Sainbayar Sukhbaatar;Arthur Szlam

  • Understanding the role of individual units in a deep neural network.

    David Bau;Jun-Yan Zhu;Hendrik Strobelt;Agata Lapedriza

Frequent Co-Authors

David Bau
David Bau Northeastern University
Xiaogang Wang
Xiaogang Wang Chinese University of Hong Kong
Xiaoou Tang
Xiaoou Tang Chinese University of Hong Kong
Dahua Lin
Dahua Lin Chinese University of Hong Kong
Jianping Shi
Jianping Shi SenseTime
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University
Hendrik Strobelt
Hendrik Strobelt IBM (United States)

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