World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
11210
World Ranking
5575
National Ranking
742

Zhenguo Li 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 Zhenguo Li 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: 190 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Zhenguo Li 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 Zhenguo Li 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: 50 D-Index — 62nd percentile

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

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

Overview

Zhenguo Li is affiliated with Huawei Technologies (China) and has a research focus primarily within the field of Computer Science. Their publication history reflects significant work in subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Computational Theory and Mathematics, and Information Systems.

The scientist's research covers a range of main topics including Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, Multimodal Machine Learning Applications, Advanced Image and Video Retrieval Techniques, Topic Modeling, Generative Adversarial Networks and Image Synthesis, as well as Anomaly Detection Techniques and Applications.

Notable recent papers authored or co-authored by Zhenguo Li include:

  • DetCo: Unsupervised Contrastive Learning for Object Detection, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model, 2024, IEEE Robotics and Automation Letters
  • FILIP: Fine-grained Interactive Language-Image Pre-Training, 2021, arXiv (Cornell University)
  • SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors collaborating with Zhenguo Li include Lanqing Hong, Hang Xu, Enze Xie, Xiaodan Liang, and Fengwei Zhou.

Publication venues with recurring contributions from Zhenguo Li include:

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

Best Publications

  • DeepFM: a factorization-machine based neural network for CTR prediction

    Huifeng Guo;Ruiming Tang;Yunming Ye;Zhenguo Li

  • Meta-SGD: Learning to Learn Quickly for Few Shot Learning.

    Zhenguo Li;Fengwei Zhou;Fei Chen;Hang Li

  • Segmentation using superpixels: A bipartite graph partitioning approach

    Zhenguo Li;Xiao-Ming Wu;Shih-Fu Chang

  • Federated Meta-Learning with Fast Convergence and Efficient Communication

    Fei Chen;Mi Luo;Zhenhua Dong;Zhenguo Li

  • DetCo: Unsupervised Contrastive Learning for Object Detection

    Enze Xie;Jian Ding;Wenhai Wang;Xiaohang Zhan

  • DARTS+: Improved Differentiable Architecture Search with Early Stopping.

    Hanwen Liang;Shifeng Zhang;Jiacheng Sun;Xingqiu He

  • Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification

    Hang Xu;Lewei Yao;Zhenguo Li;Xiaodan Liang

  • FILIP: Fine-grained Interactive Language-Image Pre-Training

    Lewei Yao;Runhui Huang;Lu Hou;Guansong Lu

  • CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending

    Hang Xu;Shaoju Wang;Xinyue Cai;Wei Zhang

  • AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

    Bin Liu;Chenxu Zhu;Guilin Li;Weinan Zhang

  • Boosting Few-Shot Learning With Adaptive Margin Loss

    Aoxue Li;Weiran Huang;Xu Lan;Jiashi Feng

  • Pairwise constraint propagation by semidefinite programming for semi-supervised classification

    Zhenguo Li;Jianzhuang Liu;Xiaoou Tang

  • An Embedding Learning Framework for Numerical Features in CTR Prediction

    Huifeng Guo;Bo Chen;Ruiming Tang;Weinan Zhang

  • Constrained clustering via spectral regularization

    Zhenguo Li;Jianzhuang Liu;Xiaoou Tang

  • Spatial-Aware Graph Relation Network for Large-Scale Object Detection

    Hang Xu;Chenhan Jiang;Xiaodan Liang;Zhenguo Li

  • One Million Scenes for Autonomous Driving: ONCE Dataset

    Jiageng Mao;Minzhe Niu;Chenhan Jiang;Hanxue Liang

  • Federated Meta-Learning for Recommendation

    Fei Chen;Zhenhua Dong;Zhenguo Li;Xiuqiang He

  • Learning with Partially Absorbing Random Walks

    Xiao-ming Wu;Zhenguo Li;Anthony M. So;John Wright

  • Deep Meta-Learning: Learning to Learn in the Concept Space

    Fengwei Zhou;Bin Wu;Zhenguo Li

  • OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization

    Unknown

  • Locally Linear Hashing for Extracting Non-linear Manifolds

    Go Irie;Zhenguo Li;Xiao-Ming Wu;Shih-Fu Chang

  • Bridging the gap between sample-based and one-shot neural architecture search with BONAS

    Han Shi;Renjie Pi;Hang Xu;Zhenguo Li

Frequent Co-Authors

Xiaodan Liang
Xiaodan Liang Sun Yat-sen University
Xiuqiang He
Xiuqiang He Huawei Technologies (China)
Shih-Fu Chang
Shih-Fu Chang Columbia University
Weinan Zhang
Weinan Zhang Shanghai Jiao Tong University
Jianzhuang Liu
Jianzhuang Liu Shenzhen Institutes of Advanced Technology
Xiaoou Tang
Xiaoou Tang Chinese University of Hong Kong
John C. S. Lui
John C. S. Lui Chinese University of Hong Kong
Jiashi Feng
Jiashi Feng ByteDance
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Yong Yu
Yong Yu Shanghai Jiao Tong University

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