World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
80
Citations
51031
World Ranking
1047
National Ranking
153

Ping Luo 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 Ping Luo 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: 210 publications — 50th percentile

50% of scientists in this discipline score the same or lower.

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

Ping Luo 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 Ping Luo 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: 80 D-Index — 93rd percentile

93% of scientists in this discipline score the same or lower.

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

Overview

Ping Luo is affiliated with the University of Hong Kong in China. Their research primarily covers the field of computer science, with a focus on areas such as computer vision and pattern recognition, artificial intelligence, molecular biology, infectious diseases, and electrical and electronic engineering.

The scientist's work spans several specialized topics including advanced neural network applications, domain adaptation and few-shot learning, multimodal machine learning applications, advanced image and video retrieval techniques, video surveillance and tracking methods, human pose and action recognition, and generative adversarial networks and image synthesis.

Among their recent publications are the following papers:

  • Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers, 2021, arXiv (Cornell University)
  • PVT v2: Improved baselines with pyramid vision transformer, 2022, Computational Visual Media
  • SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers, 2021, The Caltech Institute Archives (California Institute of Technology)
  • TransTrack: Multiple Object Tracking with Transformer, 2020, arXiv (Cornell University)

Frequent coauthors in their work include:

  • Enze Xie
  • Wenqi Shao
  • Peize Sun
  • Sheng Jin
  • Wentao Liu

The scientist has contributed extensively to a range of publication venues, with the most frequent being:

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

Best Publications

  • Deep Learning Face Attributes in the Wild

    Ziwei Liu;Ping Luo;Xiaogang Wang;Xiaoou Tang

  • Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction Without Convolutions

    Wenhai Wang;Enze Xie;Xiang Li;Deng-Ping Fan

  • SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

    Enze Xie;Wenhai Wang;Zhiding Yu;Anima Anandkumar

  • WIDER FACE: A Face Detection Benchmark

    Shuo Yang;Ping Luo;Chen Change Loy;Xiaoou Tang

  • PVTv2: Improved Baselines with Pyramid Vision Transformer

    Wenhai Wang;Enze Xie;Xiang Li;Deng-Ping Fan

  • DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

    Ziwei Liu;Ping Luo;Shi Qiu;Xiaogang Wang

  • Facial Landmark Detection by Deep Multi-task Learning

    Zhanpeng Zhang;Ping Luo;Chen Change Loy;Xiaoou Tang

  • Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

    Peize Sun;Rufeng Zhang;Yi Jiang;Tao Kong

  • Spatial as deep: Spatial CNN for traffic scene understanding

    Xingang Pan;Jianping Shi;Ping Luo;Xiaogang Wang

  • MaskGAN: Towards Diverse and Interactive Facial Image Manipulation

    Cheng-Han Lee;Ziwei Liu;Lingyun Wu;Ping Luo

  • A large-scale car dataset for fine-grained categorization and verification

    Linjie Yang;Ping Luo;Chen Change Loy;Xiaoou Tang

  • Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

    Xingang Pan;Ping Luo;Jianping Shi;Xiaoou Tang

  • Semantic Image Segmentation via Deep Parsing Network

    Ziwei Liu;Xiaoxiao Li;Ping Luo;Chen-Change Loy

  • From Facial Parts Responses to Face Detection: A Deep Learning Approach

    Shuo Yang;Ping Luo;Chen-Change Loy;Xiaoou Tang

  • PolarMask: Single Shot Instance Segmentation With Polar Representation

    Enze Xie;Peize Sun;Xiaoge Song;Wenhai Wang

  • Deep Learning Strong Parts for Pedestrian Detection

    Yonglong Tian;Ping Luo;Xiaogang Wang;Xiaoou Tang

  • DeepID-Net: Deformable deep convolutional neural networks for object detection

    Wanli Ouyang;Xiaogang Wang;Xingyu Zeng;Shi Qiu

  • Pedestrian detection aided by deep learning semantic tasks

    Yonglong Tian;Ping Luo;Xiaogang Wang;Xiaoou Tang

  • Learning Deep Representation for Face Alignment with Auxiliary Attributes

    Zhanpeng Zhang;Ping Luo;Chen Change Loy;Xiaoou Tang

  • Pedestrian Attribute Recognition At Far Distance

    Yubin Deng;Ping Luo;Chen Change Loy;Xiaoou Tang

Frequent Co-Authors

Xiaoou Tang
Xiaoou Tang Chinese University of Hong Kong
Xiaogang Wang
Xiaogang Wang Chinese University of Hong Kong
Chen Change Loy
Chen Change Loy Nanyang Technological University
Ziwei Liu
Ziwei Liu Nanyang Technological University
Liang Lin
Liang Lin Sun Yat-sen University
Enze Xie
Enze Xie Nvidia (United States)
Jianping Shi
Jianping Shi SenseTime
Wenhai Wang
Wenhai Wang Chinese University of Hong Kong
Wangmeng Zuo
Wangmeng Zuo Harbin Institute of Technology
Wanli Ouyang
Wanli Ouyang Shanghai AI Lab

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