World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
52
Citations
24936
World Ranking
4948
National Ranking
2301

Oliver 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 Oliver 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: 144 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Oliver 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 Oliver 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: 52 D-Index — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Overview

Oliver Wang is affiliated with Adobe Systems in the United States. Their research activity is centered predominantly in the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition.

Their recent research output includes several publications spanning from 2020 to 2022. Notable papers include:

  • Real-Time Semantic Segmentation With Fast Attention, 2020, IEEE Robotics and Automation Letters
  • Swapping Autoencoder for Deep Image Manipulation, 2020, arXiv (Cornell University)
  • Towards Accurate Reconstruction of 3D Scene Shape From A Single Monocular Image, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Neural Volumetric Object Selection, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes, 2020, arXiv (Cornell University)

The scientist maintains collaborations with frequent co-authors such as Fabian Caba Heilbron, Jianming Zhang, Simon Niklaus, Wei Yin, and Simon Chen.

Oliver Wang's research contributions are often published in venues including:

  • arXiv (Cornell University)
  • ACM Transactions on Graphics
  • Creativity and Cognition
  • IEEE Robotics and Automation Letters
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Their work extensively covers main topics related to advanced vision and imaging, with publications also addressing generative adversarial networks and image synthesis, computer graphics and visualization techniques, optical measurement and interference techniques, video analysis and summarization, advanced neural network applications, and image processing techniques and applications.

  • Advanced Vision and Imaging
  • Generative Adversarial Networks and Image Synthesis
  • Computer Graphics and Visualization Techniques
  • Optical measurement and interference techniques
  • Video Analysis and Summarization
  • Advanced Neural Network Applications
  • Image Processing Techniques and Applications

Their subfields of study reinforce this thematic concentration:

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence
  • Media Technology
  • Computational Mechanics

Best Publications

  • The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

    Richard Zhang;Phillip Isola;Phillip Isola;Alexei A. Efros;Eli Shechtman

  • Toward Multimodal Image-to-Image Translation

    Jun Yan Zhu;Richard Zhang;Deepak Pathak;Trevor Darrell

  • CNN-Generated Images Are Surprisingly Easy to Spot… for Now

    Sheng-Yu Wang;Oliver Wang;Richard Zhang;Andrew Owens

  • High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis

    Chao Yang;Xin Lu;Zhe Lin;Eli Shechtman

  • Multimodal Image-to-Image Translation by Enforcing Bi-Cycle Consistency

    Jun-Yan Zhu;Richard Zhang;Deepak Pathak;Trevor Darrell

  • Localizing Moments in Video with Natural Language

    Lisa Anne Hendricks;Lisa Anne Hendricks;Oliver Wang;Eli Shechtman;Josef Sivic

  • Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

    Zhengqi Li;Simon Niklaus;Noah Snavely;Oliver Wang

  • Deep Video Deblurring for Hand-Held Cameras

    Shuochen Su;Mauricio Delbracio;Jue Wang;Guillermo Sapiro

  • Nonlinear disparity mapping for stereoscopic 3D

    Manuel Lang;Alexander Hornung;Oliver Wang;Steven Poulakos

  • Learning Blind Video Temporal Consistency

    Wei Sheng Lai;Jia Bin Huang;Oliver Wang;Eli Shechtman

  • Phase-based frame interpolation for video

    Simone Meyer;Oliver Wang;Henning Zimmer;Max Grosse

  • MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks

    Animesh Karnewar;Oliver Wang

  • ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing

    Chen-Hsuan Lin;Ersin Yumer;Oliver Wang;Eli Shechtman

  • Bilateral Space Video Segmentation

    Nicolas Marki;Federico Perazzi;Oliver Wang;Alexander Sorkine-Hornung

  • Fully Connected Object Proposals for Video Segmentation

    Federico Perazzi;Oliver Wang;Markus Gross;Alexander Sorkine-Hornung

  • Temporally Distributed Networks for Fast Video Semantic Segmentation

    Ping Hu;Fabian Caba;Oliver Wang;Zhe Lin

  • Learning to Recover 3D Scene Shape from a Single Image

    Wei Yin;Jianming Zhang;Oliver Wang;Simon Niklaus

  • Swapping Autoencoder for Deep Image Manipulation

    Taesung Park;Jun-Yan Zhu;Oliver Wang;Jingwan Lu

  • Structure-Guided Ranking Loss for Single Image Depth Prediction

    Ke Xian;Jianming Zhang;Oliver Wang;Long Mai

  • Practical temporal consistency for image-based graphics applications

    Manuel Lang;Oliver Wang;Tunc Aydin;Aljoscha Smolic

  • Localizing Moments in Video with Temporal Language

    Lisa Anne Hendricks;Oliver Wang;Eli Shechtman;Josef Sivic

Frequent Co-Authors

Markus Gross
Markus Gross ETH Zurich
Eli Shechtman
Eli Shechtman Adobe Systems (United States)
Zhe Lin
Zhe Lin Adobe Systems (United States)
James Davis
James Davis University of California, Santa Cruz
Alexei A. Efros
Alexei A. Efros University of California, Berkeley
Bryan C. Russell
Bryan C. Russell Adobe Systems (United States)
Trevor Darrell
Trevor Darrell University of California, Berkeley
Ping Tan
Ping Tan Simon Fraser University
Jue Wang
Jue Wang Tencent (China)
Matthew Fisher
Matthew Fisher Adobe Systems (United States)

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