World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
22861
World Ranking
2694
National Ranking
369

Jianxin Wu 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 Jianxin Wu 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: 158 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Jianxin Wu 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 Jianxin Wu 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: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Overview

Jianxin Wu is affiliated with Nanjing University in China and primarily works in the field of Computer Science, with a significant focus on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Signal Processing, and Neurology. They have contributed extensively to these areas with numerous research publications.

Their research topics include:

  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Generative Adversarial Networks and Image Synthesis

Jianxin Wu's publication record features work in frequent venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Pattern Recognition
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • SSRN Electronic Journal

Notable recent papers by Jianxin Wu include:

  • "AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference" (2020), published in Pattern Recognition
  • "Residual Attention: A Simple but Effective Method for Multi-Label Recognition" (2021), presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks" (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Research Advances on the Damage Mechanism of Skin Glycation and Related Inhibitors" (2022), published in Nutrients
  • "Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach" (2021), presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Frequent coauthors collaborating with Jianxin Wu include:

  • Yun-Hao Cao
  • Chen-Lin Zhang
  • Ke Zhu
  • Yin-Yin He
  • Minghao Fu

In addition to journal and conference articles, Jianxin Wu has published a book titled Essentials of Pattern Recognition (2020) with Cambridge University Press.

Best Publications

  • Exploratory Undersampling for Class-Imbalance Learning

    Xu-Ying Liu;Jianxin Wu;Zhi-Hua Zhou

  • Ensembling neural networks: many could be better than all

    Zhi-Hua Zhou;Jianxin Wu;Wei Tang

  • Exploratory Under-Sampling for Class-Imbalance Learning

    Xu-Ying Liu;Jianxin Wu;Zhi-Hua Zhou

  • ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

    Jian-Hao Luo;Jianxin Wu;Weiyao Lin

  • CENTRIST: A Visual Descriptor for Scene Categorization

    Jianxin Wu;J M Rehg

  • Deep Label Distribution Learning With Label Ambiguity

    Bin-Bin Gao;Chao Xing;Chen-Wei Xie;Jianxin Wu

  • Probabilistic End-To-End Noise Correction for Learning With Noisy Labels

    Kun Yi;Jianxin Wu

  • A Scalable Approach to Activity Recognition based on Object Use

    Jianxin Wu;A. Osuntogun;T. Choudhury;M. Philipose

  • Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

    Xiu-Shen Wei;Jian-Hao Luo;Jianxin Wu;Zhi-Hua Zhou

  • Fast Asymmetric Learning for Cascade Face Detection

    Jianxin Wu;S.C. Brubaker;M.D. Mullin;J.M. Rehg

  • Minimal gated unit for recurrent neural networks

    Guo-Bing Zhou;Jianxin Wu;Chen-Lin Zhang;Zhi-Hua Zhou

  • Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization

    Xiu-Shen Wei;Chen-Wei Xie;Jianxin Wu;Chunhua Shen

  • Fine-Grained Image Analysis with Deep Learning: A Survey

    Xiu-Shen Wei;Yi-Zhe Song;Oisin Mac Aodha;Jianxin Wu

  • Beyond the Euclidean distance: Creating effective visual codebooks using the Histogram Intersection Kernel

    Jianxin Wu;James M. Rehg

  • Face recognition with one training image per person

    Jianxin Wu;Zhi-Hua Zhou

  • ThiNet: Pruning CNN Filters for a Thinner Net

    Jian-Hao Luo;Hao Zhang;Hong-Yu Zhou;Chen-Wei Xie

  • On the Design of Cascades of Boosted Ensembles for Face Detection

    S. Charles Brubaker;Jianxin Wu;Jie Sun;Matthew D. Mullin

  • AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference

    Jian-Hao Luo;Jianxin Wu

  • Genetic algorithm based selective neural network ensemble

    Zhi-Hua Zhou;Jian-Xin Wu;Yuan Jiang;Shi-Fu Chen

  • PBN: towards practical activity recognition using smartphone-based body sensor networks

    Matthew Keally;Gang Zhou;Guoliang Xing;Jianxin Wu

  • Enhanced (PC) 2 A for face recognition with one training image per person

    Songcan Chen;Daoqiang Zhang;Zhi-Hua Zhou

Frequent Co-Authors

Weiyao Lin
Weiyao Lin Shanghai Jiao Tong University
Xiu-Shen Wei
Xiu-Shen Wei Southeast University
Zhi-Hua Zhou
Zhi-Hua Zhou Nanjing University
Chunhua Shen
Chunhua Shen Zhejiang University
James M. Rehg
James M. Rehg University of Illinois at Urbana-Champaign
Jianfei Cai
Jianfei Cai Monash University
Jingdong Wang
Jingdong Wang Baidu (China)
Xin Geng
Xin Geng Southeast University
Junchi Yan
Junchi Yan Shanghai Jiao Tong University
Mingliang Xu
Mingliang Xu Zhengzhou University

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