World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
33199
World Ranking
4421
National Ranking
2066

Charless C. Fowlkes 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 Charless C. Fowlkes 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: 153 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Charless C. Fowlkes 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 Charless C. Fowlkes 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: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Charless C. Fowlkes is affiliated with the University of California, Irvine in the United States. Their research primarily spans the field of Computer Science, with a particular emphasis on Computer Vision and Pattern Recognition as well as Artificial Intelligence. Additional areas of study include Endocrinology, Diabetes and Metabolism, Media Technology, and Molecular Biology.

The scientist's core research topics include:

  • Human Pose and Action Recognition
  • Advanced Vision and Imaging
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Diabetic Foot Ulcer Assessment and Management
  • Anomaly Detection Techniques and Applications
  • Image Processing Techniques and Applications

Key recent publications by Charless C. Fowlkes demonstrate involvement in diverse applications and methodologies within vision and imaging sciences:

  • Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution II: dynamics, 2023, OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)
  • Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy, 2020, Proceedings of the National Academy of Sciences
  • Task Adaptive Parameter Sharing for Multi-Task Learning, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Fine-grained facial expression analysis using dimensional emotion model, 2020, Neurocomputing
  • High-resolution structure-function mapping of intact hearts reveals altered sympathetic control of infarct border zones, 2022, JCI Insight

Charless C. Fowlkes has collaborated frequently with several researchers, including:

  • Shu Kong
  • Avinash Ravichandran
  • Stefano Soatto
  • Zhe Wang
  • Alessandro Achille

The scientist's work has been published multiple times in notable venues, including:

  • arXiv (Cornell University)
  • 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Proceedings of the National Academy of Sciences
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Methods in Ecology and Evolution

Best Publications

  • A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

    D. Martin;C. Fowlkes;D. Tal;J. Malik

  • Contour Detection and Hierarchical Image Segmentation

    P Arbeláez;M Maire;C Fowlkes;J Malik

  • Learning to detect natural image boundaries using local brightness, color, and texture cues

    D.R. Martin;C.C. Fowlkes;J. Malik

  • Spectral grouping using the Nystrom method

    C. Fowlkes;S. Belongie;F. Chung;J. Malik

  • Globally-optimal greedy algorithms for tracking a variable number of objects

    Hamed Pirsiavash;Deva Ramanan;Charless C. Fowlkes

  • Discriminative Models for Multi-Class Object Layout

    Chaitanya Desai;Deva Ramanan;Charless C. Fowlkes

  • From contours to regions: An empirical evaluation

    Pablo Arbelaez;Michael Maire;Charless Fowlkes;Jitendra Malik

  • Using contours to detect and localize junctions in natural images

    M. Maire;P. Arbelaez;C. Fowlkes;J. Malik

  • Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation

    Golnaz Ghiasi;Charless C. Fowlkes

  • Photo Aesthetics Ranking Network with Attributes and Content Adaptation

    Shu Kong;Xiaohui Shen;Zhe L. Lin;Radomír Mech

  • Discriminative models for multi-class object layout

    Chaitanya Desai;Deva Ramanan;Charless Fowlkes

  • Low-Rank Bilinear Pooling for Fine-Grained Classification

    Shu Kong;Charless Fowlkes

  • A quantitative spatiotemporal atlas of gene expression in the Drosophila blastoderm.

    Charless C. Fowlkes;Cris L. Luengo Hendriks;Cris L. Luengo Hendriks;Soile V.E. Keränen;Soile V.E. Keränen;Gunther H. Weber;Gunther H. Weber

  • Multiresolution models for object detection

    Dennis Park;Deva Ramanan;Charless Fowlkes

  • Figure/Ground assignment in natural images

    Xiaofeng Ren;Charless C. Fowlkes;Jitendra Malik

  • Do We Need More Training Data

    Xiangxin Zhu;Carl Vondrick;Charless C. Fowlkes;Deva Ramanan

  • Do We Need More Training Data or Better Models for Object Detection

    Xiangxin Zhu;Carl Vondrick;Deva Ramanan;Charless C. Fowlkes

  • Efficient spatiotemporal grouping using the Nystrom method

    C. Fowlkes;S. Belongie;J. Malik

  • Local figure-ground cues are valid for natural images.

    Charless C. Fowlkes;David R. Martin;Jitendra Malik

  • Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model

    Golnaz Ghiasi;Charless C. Fowlkes

  • Task2Vec: Task Embedding for Meta-Learning

    Alessandro Achille;Michael Lam;Rahul Tewari;Avinash Ravichandran

  • A Database of Human Segmented Natural Images and its Application to

    David R. Martin;Charless Fowlkes;Doron Tal;Jitendra Malik

  • Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification

    David B. Keator;James H. Fallon;Anita Lakatos;Charless C. Fowlkes

Frequent Co-Authors

Jitendra Malik
Jitendra Malik University of California, Berkeley
Deva Ramanan
Deva Ramanan Carnegie Mellon University
Mark D. Biggin
Mark D. Biggin Lawrence Berkeley National Laboratory
Bernd Hamann
Bernd Hamann University of California, Davis
Michael B. Eisen
Michael B. Eisen University of California, Berkeley
Alexander T. Ihler
Alexander T. Ihler University of California, Irvine
Xiaofeng Ren
Xiaofeng Ren Alibaba Group (China)
Hans Hagen
Hans Hagen Technical University of Kaiserslautern
Serge Belongie
Serge Belongie University of Copenhagen
Ronald A. Li
Ronald A. Li University of Hong Kong

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