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Computer Science
USA
2026

D-Index & Metrics

Computer Science

D-Index
139
Citations
73955
World Ranking
72
National Ranking
42

Shih-Fu Chang 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 Shih-Fu Chang 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: 730 publications — 98th percentile

98% of scientists in this discipline score the same or lower.

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

Shih-Fu Chang 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 Shih-Fu Chang 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: 139 D-Index — 100th percentile

100% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2017 - ACM Fellow For contributions to large-scale multimedia content recognition and multimedia information retrieval
  • 2010 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2009 - IEEE Kiyo Tomiyasu Award “For contributions to Automated Image Classification.”

Overview

Shih-Fu Chang is affiliated with Columbia University in the United States and has made significant contributions in the field of computer science, particularly in computer vision and artificial intelligence. Their research spans multiple subfields including computer vision and pattern recognition, artificial intelligence, infectious diseases, biomedical engineering, and radiology, nuclear medicine and imaging.

The scientist's work covers a variety of topics, emphasizing multimodal machine learning applications, domain adaptation and few-shot learning, advanced image and video retrieval techniques, advanced neural network applications, topic modeling, human pose and action recognition, and natural language processing techniques.

Shih-Fu Chang has a number of recent publications, notable for their focus on few-shot object detection and connections between text and images. Key papers include:

  • "Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment" (2022), published in the Proceedings of the AAAI Conference on Artificial Intelligence
  • "Few-Shot Object Detection with Fully Cross-Transformer" (2022), published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks" (2021), presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "CLIP-Event: Connecting Text and Images with Event Structures" (2022), featured in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding" (2021), published in the Proceedings of the AAAI Conference on Artificial Intelligence

The frequent co-authors in their research include Jiawei Ma, Guangxing Han, Alireza Zareian, Haoxuan You, and Shiyuan Huang.

Shih-Fu Chang has published extensively in venues such as arXiv (Cornell University), the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), the IEEE/CVF International Conference on Computer Vision (ICCV), the Proceedings of the AAAI Conference on Artificial Intelligence, and bioRxiv (Cold Spring Harbor Laboratory).

Awards received by Shih-Fu Chang include the ACM Fellow in 2017 for contributions to large-scale multimedia content recognition and multimedia information retrieval, the Fellow of the American Association for the Advancement of Science (AAAS) in 2010, and the IEEE Kiyo Tomiyasu Award in 2009 for contributions to automated image classification.

Best Publications

  • VisualSEEk: a fully automated content-based image query system

    John R. Smith;Shih-Fu Chang

  • Search for dark matter and large extra dimensions in monojet events in pp collisions at √s = 7 TeV

    S. Chatrchyan;V. Khachatryan;A. M. Sirunyan;A. Tumasyan

  • Image Retrieval: Current Techniques, Promising Directions, and Open Issues

    Unknown

  • Supervised hashing with kernels

    Wei Liu;Jun Wang;Rongrong Ji;Yu-Gang Jiang

  • Hashing with Graphs

    Wei Liu;Jun Wang;Sanjiv Kumar;Shih-fu Chang

  • Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

    Zheng Shou;Dongang Wang;Shih-Fu Chang

  • Tools and techniques for color image retrieval

    John R. Smith;Shih-Fu Chang

  • Semi-Supervised Hashing for Large-Scale Search

    Jun Wang;S. Kumar;Shih-Fu Chang

  • Large-scale visual sentiment ontology and detectors using adjective noun pairs

    Damian Borth;Rongrong Ji;Tao Chen;Thomas Breuel

  • Large-scale concept ontology for multimedia

    M. Naphade;J.R. Smith;J. Tesic;Shih-Fu Chang

  • A robust image authentication method distinguishing JPEG compression from malicious manipulation

    Ching-Yung Lin;Shih-Fu Chang

  • A robust content based digital signature for image authentication

    M. Schneider;Shih-Fu Chang

  • Semi-supervised hashing for scalable image retrieval

    Jun Wang;Sanjiv Kumar;Shih-Fu Chang

  • Overview of the MPEG-7 standard

    Shih-Fu Chang;T. Sikora;A. Purl

  • Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

    Mang Ye;Xu Zhang;Pong C. Yuen;Shih-Fu Chang

  • A fully automated content-based video search engine supporting spatiotemporal queries

    Shih-Fu Chang;W. Chen;H.J. Meng;H. Sundaram

  • Large Graph Construction for Scalable Semi-Supervised Learning

    Wei Liu;Junfeng He;Shih-fu Chang

  • CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

    Zheng Shou;Jonathan Chan;Alireza Zareian;Kazuyuki Miyazawa

  • Visually searching the Web for content

    J.R. Smith;Shih-Fu Chang

  • Transform features for texture classification and discrimination in large image databases

    J.R. Smith;Shih-Fu Chang

  • Scene change detection in an MPEG-compressed video sequence

    Jianhao Meng;Yujen Juan;Shih-Fu Chang

  • Manipulation and compositing of MC-DCT compressed video

    Shih-Fu Chang;D.G. Messerschmitt

Frequent Co-Authors

John R. Smith
John R. Smith IBM (United States)
Felix X. Yu
Felix X. Yu Google (United States)
Yu-Gang Jiang
Yu-Gang Jiang Fudan University
Sanjiv Kumar
Sanjiv Kumar Google (United States)
Wei Liu
Wei Liu Tencent (China)
Lyndon Kennedy
Lyndon Kennedy Apple (United States)
Ching-Yung Lin
Ching-Yung Lin National Chi Nan University
Hari Sundaram
Hari Sundaram University of Illinois at Urbana-Champaign
Winston H. Hsu
Winston H. Hsu National Taiwan University
Lexing Xie
Lexing Xie Australian National University

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