World's Best Scientists 2026 revealed!
Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
42
Citations
7414
World Ranking
556
National Ranking
192

Computer Science

D-Index
47
Citations
11087
World Ranking
6397
National Ranking
851

Mang Ye 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 Mang Ye 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: 106 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Mang Ye 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 Mang Ye 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: 47 D-Index — 56th percentile

56% 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

  • 2025 - Research.com Rising Stars Award

Overview

Mang Ye is a researcher affiliated with Wuhan University in China, focusing primarily on the field of Computer Science. Their extensive body of work includes 261 publications, with particular emphasis on the subfields of Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Signal Processing, and Radiology, Nuclear Medicine and Imaging.

The main topics explored in their research reflect the interdisciplinary nature of their work and include:

  • Video Surveillance and Tracking Methods
  • Human Pose and Action Recognition
  • Privacy-Preserving Technologies in Data
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Face recognition and analysis

Mang Ye has contributed research published in a number of leading venues frequently, such as:

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

Their research output includes multiple papers with significant citation counts. Examples of recent works include:

  • Deep Learning for Person Re-Identification: A Survey and Outlook, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Heterogeneous Federated Learning: State-of-the-art and Research Challenges, 2023, ACM Computing Surveys
  • Channel Augmented Joint Learning for Visible-Infrared Recognition, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learning, 2020, IEEE Transactions on Information Forensics and Security
  • Cross-Modality Person Re-Identification via Modality-Aware Collaborative Ensemble Learning, 2020, IEEE Transactions on Image Processing

Collaborative efforts are a consistent feature in Mang Ye's work. Frequent co-authors include:

  • Bo Du
  • Wenke Huang
  • Pong C. Yuen
  • Jianbing Shen
  • Xian Zhong

Best Publications

  • Deep Learning for Person Re-identification: A Survey and Outlook.

    Mang Ye;Jianbing Shen;Gaojie Lin;Tao Xiang

  • Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

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

  • Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-identification

    Mang Ye;Jianbing Shen;David J. Crandall;Ling Shao

  • Heterogeneous Federated Learning: State-of-the-art and Research Challenges

    Unknown

  • Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification

    Mang Ye;Xiangyuan Lan;Jiawei Li;Pong Chi Yuen

  • Visible thermal person re-identification via dual-constrained top-ranking

    Mang Ye;Zheng Wang;Xiangyuan Lan;Pong Chi Yuen

  • Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification

    Mang Ye;Xiangyuan Lan;Zheng Wang;Pong C. Yuen

  • Channel Augmented Joint Learning for Visible-Infrared Recognition

    Mang Ye;Weijian Ruan;Bo Du;Mike Zheng Shou

  • A Survey of Open-World Person Re-Identification

    Qingming Leng;Mang Ye;Qi Tian

  • Learn from Others and Be Yourself in Heterogeneous Federated Learning

    Unknown

  • Structure-Aware Positional Transformer for Visible-Infrared Person Re-Identification

    Unknown

  • Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learning

    Mang Ye;Jianbing Shen;Ling Shao

  • Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval

    Unknown

  • Person Reidentification via Ranking Aggregation of Similarity Pulling and Dissimilarity Pushing

    Mang Ye;Chao Liang;Yi Yu;Zheng Wang

  • Dynamic Label Graph Matching for Unsupervised Video Re-identification

    Mang Ye;Andy J. Ma;Liang Zheng;Jiawei Li

  • Robust Federated Learning with Noisy and Heterogeneous Clients

    Unknown

  • Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation

    Xin Hao;Sanyuan Zhao;Mang Ye;Jianbing Shen

  • Cross-Modality Person Re-Identification via Modality-Aware Collaborative Ensemble Learning

    Mang Ye;Xiangyuan Lan;Qingming Leng;Jianbing Shen

  • Zero-Shot Person Re-identification via Cross-View Consistency

    Zheng Wang;Ruimin Hu;Chao Liang;Yi Yu

  • Grayscale Enhancement Colorization Network for Visible-infrared Person Re-identification

    Xian Zhong;Tianyou Lu;Wenxin Huang;Mang Ye

  • Dynamic Graph Co-Matching for Unsupervised Video-Based Person Re-Identification

    Mang Ye;Jiawei Li;Andy J. Ma;Liang Zheng

  • Modality-correlation-aware sparse representation for RGB-infrared object tracking

    Xiangyuan Lan;Mang Ye;Shengping Zhang;Huiyu Zhou

  • Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification.

    Zheng Wang;Mang Ye;Fan Yang;Xiang Bai

  • Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

    Xiangyuan Lan;Mang Ye;Rui Shao;Bineng Zhong

  • Augmentation Invariant and Instance Spreading Feature for Softmax Embedding

    Mang Ye;Jianbing Shen;Xu Zhang;Pong C Yuen

  • DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

    Qingxiong Tan;Mang Ye;Baoyao Yang;Siqi Liu

Frequent Co-Authors

Pong C. Yuen
Pong C. Yuen Hong Kong Baptist University
Xiangyuan Lan
Xiangyuan Lan Hong Kong Baptist University
Jianbing Shen
Jianbing Shen University of Macau
Jun Chen
Jun Chen Nankai University
Ling Shao
Ling Shao Terminus International
Grace Lai-Hung Wong
Grace Lai-Hung Wong Chinese University of Hong Kong
Shin'ichi Satoh
Shin'ichi Satoh National Institute of Informatics
Huiyu Zhou
Huiyu Zhou University of Leicester
Shih-Fu Chang
Shih-Fu Chang Columbia University
Chia-Wen Lin
Chia-Wen Lin National Tsing Hua University

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

Studying Computer Science in the USA opens the door to a range of academic options and career opportunities. As technology continues to shape our world, Computer Science remains one of the top degrees in demand for the future. Graduates are sought after across various industries, from tech startups to Fortune 500 companies.

For those looking to advance their education, there are flexible pathways such as online master’s and doctoral degrees. Some professionals opt for the easiest masters degrees which balance career ambitions with manageable workloads. If cost is a significant concern, exploring the cheapest phd programs can make higher education more accessible and affordable.

For educators or career changers interested in rapid advancement, accelerated doctoral programs in education online provide a fast-track option. These programs are ideal for motivated individuals eager to make an impact in academia or instructional technology.

Best Scientists Citing Mang Ye

Trending Scientists