World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
12010
World Ranking
8186
National Ranking
489

Xiaodong Yang 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 Xiaodong Yang 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: 100 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Xiaodong Yang 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 Xiaodong Yang 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Xiaodong Yang is affiliated with Nvidia in the United Kingdom and conducts research primarily within the broad field of computer science. Their academic work focuses predominantly on computer vision and pattern recognition, with contributions spanning related subfields such as artificial intelligence, computer graphics and computer-aided design, aerospace engineering, and media technology.

Their published work includes research themes centered on advanced vision and imaging, advanced image processing techniques, and advanced image and video retrieval techniques. Additional topics covered in their publications involve computer graphics and visualization techniques, advanced data compression techniques, image enhancement techniques, and human pose and action recognition.

Frequent publication venues for their research include:

  • arXiv (Cornell University)
  • Neurocomputing
  • International Journal of Machine Learning and Cybernetics
  • SSRN Electronic Journal
  • Intelligent Medicine

Notable recent papers authored or co-authored by Xiaodong Yang are:

  • "Hierarchical Contrastive Motion Learning for Video Action Recognition," 2020, arXiv (Cornell University)
  • "4D Gaussian Splatting for high-fidelity dynamic reconstruction of single-view scenes," 2025, Neurocomputing
  • "An adaptive joint optimization framework for pruning and quantization," 2024, International Journal of Machine Learning and Cybernetics
  • "GEDepth: Ground Embedding for Monocular Depth Estimation," 2023, arXiv (Cornell University)
  • "4d Gaussian Splatting for High-Fidelity Dynamic Reconstruction of Single-View Scenes," 2024, SSRN Electronic Journal

They have collaborated frequently with several co-authors, including:

  • Weixing Xie
  • Yihang Fu
  • Wentao Fan
  • Sen Peng
  • Baorong Yang

The distribution of their contributions indicates a strong emphasis on research at the intersection of computer vision techniques and machine learning methodologies. Their work on motion learning, depth estimation, and dynamic scene reconstruction reflects practical applications in video analytics and imaging technologies.

Best Publications

  • PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

    Deqing Sun;Xiaodong Yang;Ming-Yu Liu;Jan Kautz

  • MoCoGAN: Decomposing Motion and Content for Video Generation

    Sergey Tulyakov;Ming-Yu Liu;Xiaodong Yang;Jan Kautz

  • Joint Discriminative and Generative Learning for Person Re-Identification

    Zhedong Zheng;Xiaodong Yang;Zhiding Yu;Liang Zheng

  • Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks

    Pavlo Molchanov;Xiaodong Yang;Shalini Gupta;Kihwan Kim

  • Recognizing actions using depth motion maps-based histograms of oriented gradients

    Xiaodong Yang;Chenyang Zhang;YingLi Tian

  • EigenJoints-based action recognition using Naïve-Bayes-Nearest-Neighbor

    Xiaodong Yang;Ying Li Tian

  • Super Normal Vector for Activity Recognition Using Depth Sequences

    Xiaodong Yang;YingLi Tian

  • CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

    Zheng Tang;Milind Naphade;Ming-Yu Liu;Xiaodong Yang

  • Effective 3D action recognition using EigenJoints

    Xiaodong Yang;YingLi Tian

  • Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification.

    Yang Zou;Xiaodong Yang;Zhiding Yu;B. V. K. Vijaya Kumar

  • Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection

    Zhongzheng Ren;Zhiding Yu;Xiaodong Yang;Ming-Yu Liu

  • Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation

    Deqing Sun;Xiaodong Yang;Ming-Yu Liu;Jan Kautz

  • PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

    Zheng Tang;Milind Naphade;Stan Birchfield;Jonathan Tremblay

  • Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction

    Longlong Jing;Xiaodong Yang;Jingen Liu;Yingli Tian

  • Super Normal Vector for Human Activity Recognition with Depth Cameras

    Xiaodong Yang;YingLi Tian

  • Simulating Content Consistent Vehicle Datasets with Attribute Descent.

    Yue Yao;Liang Zheng;Xiaodong Yang;Milind Naphade

  • Robust and Effective Component-Based Banknote Recognition for the Blind

    Faiz M. Hasanuzzaman;Xiaodong Yang;YingLi Tian

  • STEP: Spatio-Temporal Progressive Learning for Video Action Detection

    Xitong Yang;Xiaodong Yang;Ming-Yu Liu;Fanyi Xiao

  • Multilayer and Multimodal Fusion of Deep Neural Networks for Video Classification

    Xiaodong Yang;Pavlo Molchanov;Jan Kautz

  • Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network

    Jinwei Gu;Xiaodong Yang;Shalini De Mello;Jan Kautz

  • Toward a Computer Vision-based Wayfinding Aid for Blind Persons to Access Unfamiliar Indoor Environments.

    YingLi Tian;Xiaodong Yang;Chucai Yi;Aries Arditi

Frequent Co-Authors

Yingli Tian
Yingli Tian City University of New York
Jan Kautz
Jan Kautz Nvidia (United States)
Ming-Yu Liu
Ming-Yu Liu Nvidia (United States)
Pavlo Molchanov
Pavlo Molchanov Nvidia (United States)
Milind R. Naphade
Milind R. Naphade Nvidia (United States)
Liang Zheng
Liang Zheng Australian National University
Zheng Tang
Zheng Tang Donghua University
Deqing Sun
Deqing Sun Google (United States)
Gal Chechik
Gal Chechik Bar-Ilan University
Kihwan Kim
Kihwan Kim Nvidia (United Kingdom)

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