World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
12800
World Ranking
3644
National Ranking
485

Guanbin Li 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 Guanbin Li 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: 188 publications — 42nd percentile

42% of scientists in this discipline score the same or lower.

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

Guanbin Li 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 Guanbin Li 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: 58 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Overview

Guanbin Li is affiliated with Sun Yat-sen University in China. Their research primarily falls within the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Oncology, and Building and Construction.

The scientist's work explores several main topics, including:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Advanced Neural Network Applications
  • Human Pose and Action Recognition
  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection

Guanbin Li has contributed extensively to academic literature, with numerous publications in prominent venues. Frequent publication venues include:

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

Some of the recent papers authored by or involving Guanbin Li include:

  • "Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering" (2023) in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction" (2020) in IEEE Transactions on Intelligent Transportation Systems
  • "Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction" (2020) in IEEE Transactions on Intelligent Transportation Systems
  • "Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules" (2022) in Computers in Biology and Medicine
  • "Cross-Modal Progressive Comprehension for Referring Segmentation" (2021) in IEEE Transactions on Pattern Analysis and Machine Intelligence

They have collaborated frequently with several co-authors, including:

  • Liang Lin
  • Yizhou Yu
  • Xiang Wan
  • Shuguang Cui
  • Haofeng Li

Best Publications

  • Visual saliency based on multiscale deep features

    Guanbin Li;Yizhou Yu

  • Deep Contrast Learning for Salient Object Detection

    Guanbin Li;Yizhou Yu

  • Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation

    Ruijia Xu;Guanbin Li;Jihan Yang;Liang Lin

  • Visual Saliency Detection Based on Multiscale Deep CNN Features

    Guanbin Li;Yizhou Yu

  • Multi-label Image Recognition by Recurrently Discovering Attentional Regions

    Zhouxia Wang;Tianshui Chen;Guanbin Li;Ruijia Xu

  • Instance-Level Salient Object Segmentation

    Guanbin Li;Yuan Xie;Liang Lin;Yizhou Yu

  • Crowd Counting With Deep Structured Scale Integration Network

    Lingbo Liu;Zhilin Qiu;Guanbin Li;Shufan Liu

  • Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction

    Lingbo Liu;Zhilin Qiu;Guanbin Li;Qing Wang

  • Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

    Guanbin Li;Xiang He;Wei Zhang;Huiyou Chang

  • Attention-Aware Face Hallucination via Deep Reinforcement Learning

    Qingxing Cao;Liang Lin;Yukai Shi;Xiaodan Liang

  • Dynamic Graph Attention for Referring Expression Comprehension

    Sibei Yang;Guanbin Li;Yizhou Yu

  • Adaptive Context Selection for Polyp Segmentation

    Ruifei Zhang;Guanbin Li;Zhen Li;Shuguang Cui

  • A Real-Time Cross-Modality Correlation Filtering Method for Referring Expression Comprehension

    Yue Liao;Si Liu;Guanbin Li;Fei Wang

  • Motion Guided Attention for Video Salient Object Detection

    Haofeng Li;Guanqi Chen;Guanbin Li;Yizhou Yu

  • Referring Image Segmentation via Cross-Modal Progressive Comprehension

    Shaofei Huang;Tianrui Hui;Si Liu;Guanbin Li

  • Crowd Counting using Deep Recurrent Spatial-Aware Network

    Lingbo Liu;Hongjun Wang;Guanbin Li;Wanli Ouyang

  • ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis

    Chao Chen;Guanbin Li;Ruijia Xu;Tianshui Chen

  • Flow Guided Recurrent Neural Encoder for Video Salient Object Detection

    Guanbin Li;Yuan Xie;Tianhao Wei;Keze Wang

  • Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction

    Lingbo Liu;Jingwen Chen;Hefeng Wu;Jiajie Zhen

  • Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition

    Tianshui Chen;Zhouxia Wang;Guanbin Li;Liang Lin

  • Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering

    Unknown

  • Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition

    Guanbin Li;Xin Zhu;Yirui Zeng;Qing Wang

Frequent Co-Authors

Liang Lin
Liang Lin Sun Yat-sen University
Yizhou Yu
Yizhou Yu University of Hong Kong
Si Liu
Si Liu Beihang University
Yuan Xie
Yuan Xie Hong Kong University of Science and Technology
Xiaoguang Han
Xiaoguang Han Chinese University of Hong Kong
Wanli Ouyang
Wanli Ouyang Shanghai AI Lab
Xiaodan Liang
Xiaodan Liang Sun Yat-sen University
Errui Ding
Errui Ding Baidu (China)
Yunchao Wei
Yunchao Wei Beijing Jiaotong University
Shuguang Cui
Shuguang Cui Chinese University of Hong Kong, Shenzhen

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 education in Computer Science opens up a range of flexible and affordable options for students. If you’re seeking a budget-friendly route, some cheap online degrees fast can help reduce education costs and shorten your timeline to graduation.

Not all traditional programs require a high GPA, and the same is true for many reputable online colleges that accept low gpa. These schools provide opportunities for motivated students to launch or restart their Computer Science studies.

For those interested in combining Computer Science with broader job opportunities, consider dual or interdisciplinary studies. Innovative roles can emerge for students who have degrees such as jobs with elementary education and environmental science degree. Blending technology with other fields is increasingly valuable.

Additionally, if your focus is on speed and efficiency, various accelerated cs degree options can help you start your tech career faster. These programs are ideal for driven learners eager to join the workforce as quickly as possible.

Best Scientists Citing Guanbin Li

Trending Scientists

Recently Published Articles