World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
71
Citations
19910
World Ranking
1776
National Ranking
101

Jungong Han 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 Jungong Han 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: 287 publications — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Jungong Han 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 Jungong Han 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: 71 D-Index — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Overview

Jungong Han is affiliated with Aberystwyth University in the United Kingdom. Their research primarily focuses on computer science, with significant contributions in computer vision and pattern recognition. The scientist has published extensively, with 557 publications in these areas.

Their scholarly work spans several subfields including:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Media Technology
  • Radiology, Nuclear Medicine and Imaging
  • Biomedical Engineering

Han's main research topics encompass diverse areas related to machine learning and image analysis:

  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Video Surveillance and Tracking Methods
  • Visual Attention and Saliency Detection
  • Human Pose and Action Recognition

Frequent co-authors who have collaborated with Han include:

  • Guiguang Ding
  • Qiang Zhang
  • Yanwei Pang
  • Nianchang Huang
  • Xinbo Gao

Han's publications have appeared in multiple well-established venues such as:

  • arXiv (Cornell University)
  • Pattern Recognition
  • IEEE Transactions on Circuits and Systems for Video Technology
  • IEEE Transactions on Image Processing
  • IEEE Transactions on Neural Networks and Learning Systems

Notable recent papers include:

  • Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • YOLOv10: Real-Time End-to-End Object Detection, 2024, arXiv (Cornell University)
  • Cross-modality deep feature learning for brain tumor segmentation, 2020, Pattern Recognition
  • FMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Best Publications

  • RepVGG: Making VGG-style ConvNets Great Again

    Xiaohan Ding;Xiangyu Zhang;Ningning Ma;Jungong Han

  • Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

    Jungong Han;Ling Shao;Dong Xu;Jamie Shotton

  • ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks

    Xiaohan Ding;Yuchen Guo;Guiguang Ding;Jungong Han

  • Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review

    Qiang Zhang;Yi Liu;Rick S. Blum;Jungong Han

  • Diverse Branch Block: Building a Convolution as an Inception-like Unit

    Xiaohan Ding;Xiangyu Zhang;Jungong Han;Guiguang Ding

  • IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

    Hui Chen;Guiguang Ding;Xudong Liu;Zijia Lin

  • Gabor Convolutional Networks

    Shangzhen Luan;Baochang Zhang;Chen Chen;Xianbin Cao

  • Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-Tailed Classification

    Liuyu Xiang;Guiguang Ding;Jungong Han

  • Cross-Modality Deep Feature Learning for Brain Tumor Segmentation

    Dingwen Zhang;Guohai Huang;Qiang Zhang;Jungong Han

  • Gabor Convolutional Networks

    Shangzhen Luan;Chen Chen;Baochang Zhang;Jungong Han

  • Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure

    Xiaohan Ding;Guiguang Ding;Yuchen Guo;Jungong Han

  • RGB-T Salient Object Detection via Fusing Multi-Level CNN Features

    Qiang Zhang;Nianchang Huang;Lin Yao;Dingwen Zhang

  • Cross-View Retrieval via Probability-Based Semantics-Preserving Hashing

    Zijia Lin;Guiguang Ding;Jungong Han;Jianmin Wang

  • Automatic video-based human motion analyzer for consumer surveillance system

    Weilun Lao;Jungong Han

  • ABMDRNet: Adaptive-weighted Bi-directional Modality Difference Reduction Network for RGB-T Semantic Segmentation

    Qiang Zhang;Shenlu Zhao;Yongjiang Luo;Dingwen Zhang

  • RGB-D datasets using microsoft kinect or similar sensors: a survey

    Ziyun Cai;Jungong Han;Li Liu;Ling Shao

  • Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining

    Dingwen Zhang;Junwei Han;Jungong Han;Ling Shao

  • Episode-Based Prototype Generating Network for Zero-Shot Learning

    Yunlong Yu;Zhong Ji;Jungong Han;Zhongfei Zhang

  • Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier

    Baochang Zhang;Yun Yang;Chen Chen;Linlin Yang

  • From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis

    Yang Long;Li Liu;Ling Shao;Fumin Shen

  • Exploring Task Structure for Brain Tumor Segmentation From Multi-Modality MR Images

    Dingwen Zhang;Guohai Huang;Qiang Zhang;Jungong Han

  • Global Sparse Momentum SGD for Pruning Very Deep Neural Networks

    Xiaohan Ding;guiguang ding;Xiangxin Zhou;Yuchen Guo

  • Memory Attention Networks for Skeleton-based Action Recognition

    Chunyu Xie;Ce Li;Baochang Zhang;Chen Chen

Frequent Co-Authors

Guiguang Ding
Guiguang Ding Tsinghua University
Ling Shao
Ling Shao Terminus International
Baochang Zhang
Baochang Zhang Beihang University
Yanwei Pang
Yanwei Pang Tianjin University
Junwei Han
Junwei Han Northwestern Polytechnical University
Sicheng Zhao
Sicheng Zhao Tsinghua University
Yue Gao
Yue Gao Tsinghua University
Quanxue Gao
Quanxue Gao Xidian University
Qiang Zhang
Qiang Zhang Dalian University of Technology
Jianzhuang Liu
Jianzhuang Liu Shenzhen Institutes of Advanced Technology

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