World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
26622
World Ranking
1647
National Ranking
223

Limin Wang 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 Limin Wang 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: 292 publications — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Limin Wang 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 Limin Wang 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: 72 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

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

Overview

Limin Wang is affiliated with Nanjing University in China and specializes in the field of Computer Science, with a particular focus on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Computer Graphics and Computer-Aided Design, and Computational Theory and Mathematics.

Their research addresses key topics including Human Pose and Action Recognition, Multimodal Machine Learning Applications, Anomaly Detection Techniques and Applications, Domain Adaptation and Few-Shot Learning, Video Surveillance and Tracking Methods, Advanced Image and Video Retrieval Techniques, and Advanced Neural Network Applications.

Limin Wang has published extensively, with recurring appearances in certain publication venues. Frequent venues include arXiv (Cornell University) with 105 publications, IEEE Transactions on Pattern Analysis and Machine Intelligence with 10 publications, the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) with 7 publications, the International Journal of Computer Vision with 7 publications, and the 2021 IEEE/CVF International Conference on Computer Vision (ICCV) with 6 publications.

Recent notable papers by Limin Wang include:

  • MixFormer: End-to-End Tracking with Iterative Mixed Attention, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • A review of convolutional neural networks in computer vision, 2024, Artificial Intelligence Review
  • VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training, 2022, arXiv (Cornell University)
  • PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • TEINet: Towards an Efficient Architecture for Video Recognition, 2020, Proceedings of the AAAI Conference on Artificial Intelligence

The scientist collaborates frequently with several co-authors, including Gangshan Wu (53 co-authored papers), Yu Qiao (27), Yali Wang (22), Zhaoyang Liu (11), and Kunchang Li (10).

Best Publications

  • Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

    Limin Wang;Yuanjun Xiong;Zhe Wang;Yu Qiao

  • Action recognition with trajectory-pooled deep-convolutional descriptors

    Limin Wang;Yu Qiao;Xiaoou Tang

  • Temporal Segment Networks for Action Recognition in Videos

    Limin Wang;Yuanjun Xiong;Zhe Wang;Yu Qiao

  • Bag of visual words and fusion methods for action recognition

    Xiaojiang Peng;Limin Wang;Xingxing Wang;Yu Qiao

  • MixFormer: End-to-End Tracking with Iterative Mixed Attention

    Unknown

  • VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

    Unknown

  • Temporal Action Detection with Structured Segment Networks

    Yue Zhao;Yuanjun Xiong;Limin Wang;Zhirong Wu

  • A review of convolutional neural networks in computer vision

    Unknown

  • TEA: Temporal Excitation and Aggregation for Action Recognition

    Yan Li;Bin Ji;Xintian Shi;Jianguo Zhang

  • UntrimmedNets for Weakly Supervised Action Recognition and Detection

    Limin Wang;Yuanjun Xiong;Dahua Lin;Luc Van Gool

  • Towards Good Practices for Very Deep Two-Stream ConvNets

    Limin Wang;Yuanjun Xiong;Zhe Wang;Yu Qiao

  • Real-Time Action Recognition with Enhanced Motion Vector CNNs

    Bowen Zhang;Limin Wang;Zhe Wang;Yu Qiao

  • TDN: Temporal Difference Networks for Efficient Action Recognition

    Unknown

  • Appearance-and-Relation Networks for Video Classification

    Limin Wang;Wei Li;Luc Van Gool

  • Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

    Limin Wang;Yuanjun Xiong;Zhe Wang;Yu Qiao

  • VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

    Unknown

  • PyMAF: 3D Human Pose and Shape Regression With Pyramidal Mesh Alignment Feedback Loop

    Hongwen Zhang;Yating Tian;Xinchi Zhou;Wanli Ouyang

  • Learning Actor Relation Graphs for Group Activity Recognition

    Unknown

  • TEINet: Towards an Efficient Architecture for Video Recognition

    Zhaoyang Liu;Donghao Luo;Yabiao Wang;Limin Wang

  • Multi-view Super Vector for Action Recognition

    Zhuowei Cai;Limin Wang;Xiaojiang Peng;Yu Qiao

  • Motionlets: Mid-level 3D Parts for Human Motion Recognition

    LiMin Wang;Yu Qiao;Xiaoou Tang

  • Temporal Action Detection with Structured Segment Networks

    Yue Zhao;Yuanjun Xiong;Yuanjun Xiong;Limin Wang;Zhirong Wu;Zhirong Wu

  • A comparative study of encoding, pooling and normalization methods for action recognition

    Xingxing Wang;LiMin Wang;Yu Qiao

  • CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016.

    Yuanjun Xiong;Limin Wang;Zhe Wang;Bowen Zhang

  • Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs

    Bowen Zhang;Limin Wang;Zhe Wang;Yu Qiao

  • Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs

    Limin Wang;Sheng Guo;Weilin Huang;Yuanjun Xiong

  • A Pursuit of Temporal Accuracy in General Activity Detection

    Yuanjun Xiong;Yue Zhao;Limin Wang;Dahua Lin

  • Video Action Detection with Relational Dynamic-Poselets

    Limin Wang;Limin Wang;Yu Qiao;Xiaoou Tang;Xiaoou Tang

  • Places205-VGGNet Models for Scene Recognition

    Limin Wang;Sheng Guo;Weilin Huang;Yu Qiao

Frequent Co-Authors

Yu Qiao
Yu Qiao Chinese Academy of Sciences
Xiaoou Tang
Xiaoou Tang Chinese University of Hong Kong
Luc Van Gool
Luc Van Gool Institute for Computer Science, Artificial Intelligence and Technology (INSAIT)
Yuanjun Xiong
Yuanjun Xiong Chinese University of Hong Kong
Dahua Lin
Dahua Lin Chinese University of Hong Kong
Wen Li
Wen Li University of Electronic Science and Technology of China
Jie Song
Jie Song Hong Kong University of Science and Technology
Otmar Hilliges
Otmar Hilliges ETH Zurich
Abhinav Gupta
Abhinav Gupta Carnegie Mellon University
Rahul Sukthankar
Rahul Sukthankar Google (United States)

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