World's Best Scientists 2026 revealed!
Peng Wang

Peng Wang

D-Index & Metrics

Computer Science

D-Index
35
Citations
7182
World Ranking
11513
National Ranking
1428

Peng 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 Peng 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: 68 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

Peng 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 Peng 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Peng Wang is affiliated with Baidu in China and specializes in research within the field of Computer Science. Their work primarily focuses on subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Human-Computer Interaction, and Media Technology.

The scientist's research areas cover various topics such as Multimodal Machine Learning Applications, Video Surveillance and Tracking Methods, Human Pose and Action Recognition, Advanced Neural Network Applications, Advanced Image and Video Retrieval Techniques, Domain Adaptation and Few-Shot Learning, and Anomaly Detection Techniques and Applications.

Peng Wang has contributed to several recent publications, including:

  • "AR/MR Remote Collaboration on Physical Tasks: A Review," 2021, Robotics and Computer-Integrated Manufacturing
  • "Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond," 2023, arXiv (Cornell University)
  • "A Robust Attentional Framework for License Plate Recognition in the Wild," 2020, IEEE Transactions on Intelligent Transportation Systems
  • "VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection," 2024, Proceedings of the AAAI Conference on Artificial Intelligence
  • "3DGAM: using 3D gesture and CAD models for training on mixed reality remote collaboration," 2020, Multimedia Tools and Applications

The frequent co-authors of Peng Wang's research include Yanning Zhang, Qi Wu, Wei Suo, Lingqiao Liu, and Peng Wu.

Peng Wang's work has been regularly published in a range of venues. Venues with notable frequency include arXiv (Cornell University), IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and Pattern Recognition.

Best Publications

  • The ApolloScape Dataset for Autonomous Driving

    Xinyu Huang;Xinjing Cheng;Qichuan Geng;Binbin Cao

  • The ApolloScape Open Dataset for Autonomous Driving and Its Application

    Xinyu Huang;Peng Wang;Xinjing Cheng;Dingfu Zhou

  • Towards unified depth and semantic prediction from a single image

    Peng Wang;Xiaohui Shen;Zhe Lin;Scott Cohen

  • MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

    Liang-Chieh Chen;Alexander Hermans;George Papandreou;Florian Schroff

  • Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

    Xinjing Cheng;Peng Wang;Ruigang Yang

  • Learning Depth with Convolutional Spatial Propagation Network

    Xinjing Cheng;Peng Wang;Ruigang Yang

  • Occlusion Aware Unsupervised Learning of Optical Flow

    Yang Wang;Yi Yang;Zhenheng Yang;Liang Zhao

  • Semantic Instance Segmentation via Deep Metric Learning

    Alireza Fathi;Zbigniew Wojna;Vivek Rathod;Peng Wang

  • Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding

    Chenxu Luo;Zhenheng Yang;Peng Wang;Yang Wang

  • Joint Multi-person Pose Estimation and Semantic Part Segmentation

    Fangting Xia;Peng Wang;Xianjie Chen;Alan L. Yuille

  • CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion

    Xinjing Cheng;Peng Wang;Chenye Guan;Ruigang Yang

  • LEGO: Learning Edge with Geometry all at Once by Watching Videos

    Zhenheng Yang;Peng Wang;Yang Wang;Wei Xu

  • ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving

    Xibin Song;Peng Wang;Dingfu Zhou;Rui Zhu

  • UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos

    Yang Wang;Peng Wang;Zhenheng Yang;Chenxu Luo

  • Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net

    Fangting Xia;Peng Wang;Liang-Chieh Chen;Alan L. Yuille

  • Structure-Sensitive Superpixels via Geodesic Distance

    Peng Wang;Gang Zeng;Rui Gan;Jingdong Wang

  • Unsupervised Learning of Geometry From Videos With Edge-Aware Depth-Normal Consistency.

    Zhenheng Yang;Peng Wang;Wei Xu;Liang Zhao

  • Joint Object and Part Segmentation Using Deep Learned Potentials

    Peng Wang;Xiaohui Shen;Zhe Lin;Scott Cohen

  • SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting

    Yuhang Song;Chao Yang;Yeji Shen;Peng Wang

  • Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency.

    Zhenheng Yang;Peng Wang;Wei Xu;Liang Zhao

  • Structure-sensitive superpixels via geodesic distance

    Gang Zeng;Peng Wang;Jingdong Wang;Rui Gan

Frequent Co-Authors

Ruigang Yang
Ruigang Yang University of Kentucky
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Wei Xu
Wei Xu Horizon Robotics Inc.
Zhe Lin
Zhe Lin Adobe Systems (United States)
Xiaohui Shen
Xiaohui Shen ByteDance
Brian Price
Brian Price Adobe Systems (United States)
Scott Cohen
Scott Cohen Adobe Systems (United States)
Jingdong Wang
Jingdong Wang Baidu (China)
Gang Zeng
Gang Zeng Peking University

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