World's Best Scientists 2026 revealed!
Yu-Gang Jiang

Yu-Gang Jiang

D-Index & Metrics

Computer Science

D-Index
81
Citations
25844
World Ranking
1028
National Ranking
146

Yu-Gang Jiang 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 Yu-Gang Jiang 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: 315 publications — 76th percentile

76% of scientists in this discipline score the same or lower.

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

Yu-Gang Jiang 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 Yu-Gang Jiang 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: 81 D-Index — 93rd percentile

93% of scientists in this discipline score the same or lower.

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

Overview

Yu-Gang Jiang is affiliated with Fudan University in China and has a significant publication record in the field of computer science, with a particular focus on computer vision and pattern recognition and artificial intelligence. Their scholarly output demonstrates a sustained engagement with topics related to multimodal machine learning, domain adaptation, human pose and action recognition, adversarial robustness, and advanced neural network applications.

The scientist's recent papers include the following works:

  • Two-dimensional materials for next-generation computing technologies, 2020, published in Nature Nanotechnology
  • Cross-Domain Contrastive Learning for Unsupervised Domain Adaptation, 2022, published in IEEE Transactions on Multimedia
  • BEVT: BERT Pretraining of Video Transformers, 2022, presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • SVTR: Scene Text Recognition with a Single Visual Model, 2022, presented at the Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
  • Balanced Contrastive Learning for Long-Tailed Visual Recognition, 2022, presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors in Yu-Gang Jiang's research include:

  • Zuxuan Wu
  • Jingjing Chen
  • Xingjun Ma
  • Yanwei Fu
  • Shaoxiang Chen

The scientist has extensively published in several venues, reflecting a presence in both conference proceedings and journals. The main publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Transactions on Image Processing

Yu-Gang Jiang's research encompasses a broad range of topics within computer science and its subfields. These areas include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Signal Processing
  • Molecular Biology

Their main topics of work cover:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Human Pose and Action Recognition
  • Adversarial Robustness in Machine Learning
  • Advanced Neural Network Applications
  • Video Analysis and Summarization
  • Advanced Image and Video Retrieval Techniques

Yu-Gang Jiang has also contributed to book publications, including a work published by Springer International Publishing titled Deep Learning for Video Understanding, scheduled for 2024.

Best Publications

  • Supervised hashing with kernels

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

  • Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

    Nanyang Wang;Yinda Zhang;Zhuwen Li;Yanwei Fu

  • Evaluating bag-of-visual-words representations in scene classification

    Jun Yang;Yu-Gang Jiang;Alexander G. Hauptmann;Chong-Wah Ngo

  • Towards optimal bag-of-features for object categorization and semantic video retrieval

    Yu-Gang Jiang;Chong-Wah Ngo;Jun Yang

  • DSOD: Learning Deeply Supervised Object Detectors from Scratch

    Zhiqiang Shen;Zhuang Liu;Jianguo Li;Yu-Gang Jiang

  • The THUMOS challenge on action recognition for videos “in the wild”

    Haroon Idrees;Amir Roshan Zamir;Yu-Gang Jiang;Alex Gorban

  • NAIS: Neural Attentive Item Similarity Model for Recommendation

    Xiangnan He;Zhankui He;Jingkuan Song;Zhenguang Liu

  • Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

    Zuxuan Wu;Xi Wang;Yu-Gang Jiang;Hao Ye

  • Learning Fashion Compatibility with Bidirectional LSTMs

    Xintong Han;Zuxuan Wu;Yu-Gang Jiang;Larry S. Davis

  • Pose-Normalized Image Generation for Person Re-identification

    Xuelin Qian;Yanwei Fu;Tao Xiang;Wenxuan Wang

  • Multi-Level Semantic Feature Augmentation for One-Shot Learning

    Zitian Chen;Yanwei Fu;Yinda Zhang;Yu-Gang Jiang

  • The MediaMill TRECVID 2011 Semantic Video Search Engine

    C. G. M. Snoek;K. E. A. van de Sande;X. Li;M. Mazloom

  • Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks

    Yu-Gang Jiang;Zuxuan Wu;Jun Wang;Xiangyang Xue

  • Consumer video understanding: a benchmark database and an evaluation of human and machine performance

    Yu-Gang Jiang;Guangnan Ye;Shih-Fu Chang;Daniel Ellis

  • WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection

    Bojia Zi;Minghao Chang;Jingjing Chen;Xingjun Ma

  • Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study

    Yu-Gang Jiang;Jun Yang;Chong-Wah Ngo;A.G. Hauptmann

  • Multi-scale Deep Learning Architectures for Person Re-identification

    Xuelin Qian;Yanwei Fu;Yu-Gang Jiang;Tao Xiang

  • Recurrent Fusion Network for Image Captioning

    Wenhao Jiang;Lin Ma;Yu-Gang Jiang;Wei Liu

  • Trajectory-Based modeling of human actions with motion reference points

    Yu-Gang Jiang;Qi Dai;Xiangyang Xue;Wei Liu

  • M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection.

    Junke Wang;Zuxuan Wu;Jingjing Chen;Yu-Gang Jiang

  • High-Level Event Recognition in Unconstrained Videos

    Yu-Gang Jiang;Subhabrata Bhattacharya;Shih-Fu Chang;Mubarak Shah

Frequent Co-Authors

Xiangyang Xue
Xiangyang Xue Fudan University
Yanwei Fu
Yanwei Fu Fudan University
Chong-Wah Ngo
Chong-Wah Ngo Singapore Management University
Zuxuan Wu
Zuxuan Wu Fudan University
Shih-Fu Chang
Shih-Fu Chang Columbia University
Wei Liu
Wei Liu Tencent (China)
Leonid Sigal
Leonid Sigal University of British Columbia
Tat-Seng Chua
Tat-Seng Chua National University of Singapore
Larry S. Davis
Larry S. Davis University of Maryland, College Park
Jinhui Tang
Jinhui Tang Nanjing University of Science and Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degrees in technology fields broadens your career opportunities. For example, pursuing an online electrical engineering career outcomes page reveals how specializations can lead to roles in renewable energy, telecommunications, and automation.

If you’re seeking quick entry into the tech workforce, consider 3-month certificate programs that pay well. These short-term certifications offer concentrated skill development in areas like IT support, cybersecurity, or data analysis—ideal for jumpstarting a career or upskilling fast.

For advanced learners, there are accelerated options such as the shortest online masters degree programs. Completing a master’s online in as little as 12 months can quickly boost your credentials and earning potential without taking years away from the workforce.

When planning your academic journey, it’s crucial to invest in education that translates directly into demand. Explore recommended masters degrees that are worth it for a strategic edge in today’s competitive job market.

Best Scientists Citing Yu-Gang Jiang

Trending Scientists

Recently Published Articles