World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
6544
World Ranking
12469
National Ranking
1532

Meina Kan 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 Meina Kan 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: 72 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Meina Kan 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 Meina Kan 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Meina Kan is affiliated with the Chinese Academy of Sciences in China and specializes primarily in the field of Computer Science, with a concentration on Computer Vision and Pattern Recognition. Their research portfolio includes 52 publications in this domain, with significant contributions to Artificial Intelligence, Industrial and Manufacturing Engineering, Signal Processing, and Building and Construction.

Their work centers on several key topics, including:

  • Domain Adaptation and Few-Shot Learning
  • Face recognition and analysis
  • Advanced Neural Network Applications
  • Multimodal Machine Learning Applications
  • Generative Adversarial Networks and Image Synthesis
  • Industrial Vision Systems and Defect Detection
  • Face and Expression Recognition

Recent publications by Meina Kan include:

  • "Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning," 2020, IEEE Transactions on Neural Networks and Learning Systems
  • "Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation," 2020, arXiv (Cornell University)
  • "Learning pseudo labels for semi-and-weakly supervised semantic segmentation," 2022, Pattern Recognition
  • "Mutual Learning of Joint and Separate Domain Alignments for Multi-Source Domain Adaptation," 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • "PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing," 2020, arXiv (Cornell University)

Their frequent publication venues consist of:

  • arXiv (Cornell University)
  • Journal of Image and Graphics
  • IEEE Transactions on Neural Networks and Learning Systems
  • Pattern Recognition
  • 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Meina Kan has collaborated often with several coauthors, including:

  • Shiguang Shan (23 joint works)
  • Xilin Chen (13 joint works)
  • Zhenliang He (4 joint works)
  • Chunrui Han (3 joint works)
  • Xingguang Song (3 joint works)

In addition to journal and conference papers, Kan has contributed to book publications. One recorded book publication is Biometric Recognition published by Springer Science+Business Media in 2022, which has received citations in the academic community.

Best Publications

  • AttGAN: Facial Attribute Editing by Only Changing What You Want

    Zhenliang He;Wangmeng Zuo;Meina Kan;Shiguang Shan

  • Multi-View Discriminant Analysis

    Meina Kan;Shiguang Shan;Haihong Zhang;Shihong Lao

  • Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

    Yude Wang;Jie Zhang;Meina Kan;Shiguang Shan

  • Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment

    Jie Zhang;Shiguang Shan;Meina Kan;Xilin Chen

  • Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses

    Meina Kan;Shiguang Shan;Hong Chang;Xilin Chen

  • Multi-view Deep Network for Cross-View Classification

    Meina Kan;Shiguang Shan;Xilin Chen

  • Duplex Generative Adversarial Network for Unsupervised Domain Adaptation

    Lanqing Hu;Meina Kan;Shiguang Shan;Xilin Chen

  • Generative Adversarial Network with Spatial Attention for Face Attribute Editing

    Gang Zhang;Meina Kan;Meina Kan;Shiguang Shan;Shiguang Shan;Xilin Chen

  • AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation

    Xin Liu;Shaoxin Li;Meina Kan;Jie Zhang

  • Multi-view discriminant analysis

    Meina Kan;Shiguang Shan;Haihong Zhang;Shihong Lao

  • Funnel-structured cascade for multi-view face detection with alignment-awareness

    Shuzhe Wu;Meina Kan;Zhenliang He;Shiguang Shan

  • Occlusion-Free Face Alignment: Deep Regression Networks Coupled with De-Corrupt AutoEncoders

    Jie Zhang;Meina Kan;Shiguang Shan;Xilin Chen

  • Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

    Xuepeng Shi;Shiguang Shan;Meina Kan;Shuzhe Wu

  • Domain Adaptation for Face Recognition: Targetize Source Domain Bridged by Common Subspace

    Meina Kan;Junting Wu;Shiguang Shan;Xilin Chen

  • Weakly Supervised Object Detection With Segmentation Collaboration

    Xiaoyan Li;Meina Kan;Shiguang Shan;Xilin Chen

  • Learning to Learn Adaptive Classifier–Predictor for Few-Shot Learning

    Nan Lai;Meina Kan;Chunrui Han;Xingguang Song

  • Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization

    Lanqing Hu;Meina Kan;Shiguang Shan;Xilin Chen

  • Side-Information based Linear Discriminant Analysis for Face Recognition.

    Meina Kan;Shiguang Shan;Dong Xu;Xilin Chen

  • Fully Learnable Group Convolution for Acceleration of Deep Neural Networks

    Xijun Wang;Meina Kan;Shiguang Shan;Xilin Chen

  • Adaptive discriminant learning for face recognition

    Meina Kan;Shiguang Shan;Yu Su;Dong Xu

  • VIPLFaceNet: an open source deep face recognition SDK

    Xin Liu;Meina Kan;Wanglong Wu;Shiguang Shan

Frequent Co-Authors

Shiguang Shan
Shiguang Shan Chinese Academy of Sciences
Xilin Chen
Xilin Chen University of Chinese Academy of Sciences
Dong Xu
Dong Xu University of Hong Kong
Wangmeng Zuo
Wangmeng Zuo Harbin Institute of Technology
Shihong Lao
Shihong Lao SenseTime
Wen Gao
Wen Gao Peking University
Dacheng Tao
Dacheng Tao Nanyang Technological University
P. Jonathon Phillips
P. Jonathon Phillips National Institute of Standards and Technology
Bruce A. Draper
Bruce A. Draper Colorado State University
Josef Kittler
Josef Kittler University of Surrey

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