World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
13499
World Ranking
5515
National Ranking
2518

Jianlong Fu 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 Jianlong Fu 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: 181 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

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

Jianlong Fu 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 Jianlong Fu 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Jianlong Fu is affiliated with Microsoft in the United States and has contributed extensively to the field of computer science, with a focus on computer vision and pattern recognition. Their body of work includes 229 publications, predominantly in computer science, with significant involvement in subfields such as computer vision and pattern recognition, artificial intelligence, media technology, control and systems engineering, and electrical and electronic engineering.

Their research encompasses a variety of specialized topics, notably:

  • Multimodal Machine Learning Applications
  • Advanced Image Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Generative Adversarial Networks and Image Synthesis
  • Advanced Image and Video Retrieval Techniques
  • Human Pose and Action Recognition
  • Advanced Neural Network Applications

Jianlong Fu has frequently published in several prominent venues, including:

  • arXiv (Cornell University), with 60 publications
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 5 publications
  • IEEE Transactions on Image Processing, 4 publications
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, 4 publications
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3 publications

Some of the notable recent papers authored by Jianlong Fu include:

  • Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Rethinking and Improving Relative Position Encoding for Vision Transformer, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers, 2020, arXiv (Cornell University)
  • AutoFormer: Searching Transformers for Visual Recognition, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Revisiting Anchor Mechanisms for Temporal Action Localization, 2020, IEEE Transactions on Image Processing

Their frequent collaborators include Houwen Peng with 24 joint works, Bei Liu with 16, Huan Yang with 13, and Hongyang Chao with 10 coauthored publications.

Best Publications

  • Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition

    Jianlong Fu;Heliang Zheng;Tao Mei

  • Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition

    Heliang Zheng;Jianlong Fu;Tao Mei;Jiebo Luo

  • Learning Texture Transformer Network for Image Super-Resolution

    Fuzhi Yang;Huan Yang;Jianlong Fu;Hongtao Lu

  • Ocean: Object-aware Anchor-free Tracking

    Zhipeng Zhang;Houwen Peng;Jianlong Fu;Bing Li

  • Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition

    Heliang Zheng;Jianlong Fu;Zheng-Jun Zha;Jiebo Luo

  • Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language.

    Songyang Zhang;Houwen Peng;Jianlong Fu;Jiebo Luo

  • Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting

    Yanhong Zeng;Jianlong Fu;Hongyang Chao;Baining Guo

  • The Seventh Visual Object Tracking VOT2019 Challenge Results

    Matej Kristan;Amanda Berg;Linyu Zheng;Litu Rout

  • Rethinking and Improving Relative Position Encoding for Vision Transformer.

    Kan Wu;Houwen Peng;Minghao Chen;Jianlong Fu

  • Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers

    Zhicheng Huang;Zhaoyang Zeng;Bei Liu;Dongmei Fu

  • AutoFormer: Searching Transformers for Visual Recognition

    Minghao Chen;Houwen Peng;Jianlong Fu;Haibin Ling

  • Learning Joint Spatial-Temporal Transformations for Video Inpainting.

    Yanhong Zeng;Jianlong Fu;Hongyang Chao

  • Multi-level Attention Networks for Visual Question Answering

    Dongfei Yu;Jianlong Fu;Tao Mei;Yong Rui

  • TinyViT: Fast Pretraining Distillation for Small Vision Transformers

    Unknown

  • Expanding Language-Image Pretrained Models for General Video Recognition

    Unknown

  • The Eighth Visual Object Tracking VOT2020 Challenge Results

    Matej Kristan;Aleš Leonardis;Jiří Matas;Michael Felsberg

  • Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

    Zhicheng Huang;Zhaoyang Zeng;Yupan Huang;Bei Liu

  • LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search

    Bin Yan;Houwen Peng;Kan Wu;Dong Wang

  • Revisiting Anchor Mechanisms for Temporal Action Localization

    Le Yang;Houwen Peng;Dingwen Zhang;Jianlong Fu

  • Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner

    Tseng-Hung Chen;Yuan-Hong Liao;Ching-Yao Chuang;Wan-Ting Hsu

  • Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions

    Unknown

  • DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks

    Shuang Ma;Jianlong Fu;Chang Wen Chen;Tao Mei

  • DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)

    Shuang Ma;Jianlong Fu;Chang Wen Chen;Tao Mei

  • Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition (ICCV 2017 Oral)

    Heliang Zheng;Jianlong Fu;Tao Mei;Jiebo Luo

Frequent Co-Authors

Houwen Peng
Houwen Peng Microsoft (United States)
Tao Mei
Tao Mei Jingdong (China)
Jiebo Luo
Jiebo Luo University of Rochester
Hongyang Chao
Hongyang Chao Sun Yat-sen University
Jinqiao Wang
Jinqiao Wang Chinese Academy of Sciences
Hanqing Lu
Hanqing Lu Chinese Academy of Sciences
Zheng-Jun Zha
Zheng-Jun Zha University of Science and Technology of China
Haibin Ling
Haibin Ling Westlake University
Min Sun
Min Sun National Tsing Hua University
Houqiang Li
Houqiang Li University of Science and Technology of China

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