World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
16169
World Ranking
5489
National Ranking
77

Guosheng Lin 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 Guosheng Lin 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: 177 publications — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Guosheng Lin 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 Guosheng Lin 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

Guosheng Lin is affiliated with Nanyang Technological University in Singapore and has a substantial body of research within the fields of Computer Science and Engineering. Their work predominantly focuses on Computer Vision and Pattern Recognition, alongside significant contributions to Artificial Intelligence, Computational Mechanics, Computer Graphics and Computer-Aided Design, and Geology.

Their research spans several main topics, including:

  • 3D Shape Modeling and Analysis
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Multimodal Machine Learning Applications
  • Advanced Image and Video Retrieval Techniques
  • Advanced Vision and Imaging
  • Human Pose and Action Recognition

Guosheng Lin has authored numerous publications, frequently contributing to journals and conferences such as arXiv (Cornell University), IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, SSRN Electronic Journal, and IEEE Transactions on Multimedia.

Their recent notable papers include:

  • Video Object Segmentation and Tracking, 2020, ACM Transactions on Intelligent Systems and Technology
  • Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Learning Meta-class Memory for Few-Shot Semantic Segmentation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • A Unified Transformer Framework for Group-Based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection, 2023, IEEE Transactions on Multimedia
  • DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence

Collaboration has been a significant aspect of Guosheng Lin's research, with frequent co-authors including Fayao Liu, Qingyao Wu, Zhiguo Cao, Weide Liu, and Zhonghua Wu. These partnerships have contributed to a diverse and robust research portfolio.

Best Publications

  • RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation

    Guosheng Lin;Anton Milan;Chunhua Shen;Ian Reid

  • Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

    Fayao Liu;Chunhua Shen;Guosheng Lin;Ian Reid

  • Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

    Guosheng Lin;Chunhua Shen;Anton van den Hengel;Ian Reid

  • Deep convolutional neural fields for depth estimation from a single image

    Fayao Liu;Chunhua Shen;Guosheng Lin

  • DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers

    Chi Zhang;Yujun Cai;Guosheng Lin;Chunhua Shen

  • CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning

    Chi Zhang;Guosheng Lin;Fayao Liu;Rui Yao

  • Fast Supervised Hashing with Decision Trees for High-Dimensional Data

    Guosheng Lin;Chunhua Shen;Qinfeng Shi;Anton van den Hengel

  • Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation

    Chi Zhang;Guosheng Lin;Fayao Liu;Jiushuang Guo

  • Few-Shot Incremental Learning with Continually Evolved Classifiers

    Chi Zhang;Nan Song;Guosheng Lin;Yun Zheng

  • CRNet: Cross-Reference Networks for Few-Shot Segmentation

    Weide Liu;Chi Zhang;Guosheng Lin;Fayao Liu

  • CRF learning with CNN features for image segmentation

    Fayao Liu;Guosheng Lin;Chunhua Shen

  • Video Object Segmentation and Tracking: A Survey

    Rui Yao;Guosheng Lin;Shixiong Xia;Jiaqi Zhao

  • A General Two-Step Approach to Learning-Based Hashing

    Guosheng Lin;Chunhua Shen;David Suter;Anton Van Den Hengel

  • Progressive Modality Reinforcement for Human Multimodal Emotion Recognition from Unaligned Multimodal Sequences

    Fengmao Lv;Xiang Chen;Yanyong Huang;Lixin Duan

  • Exploring Context with Deep Structured Models for Semantic Segmentation

    Guosheng Lin;Chunhua Shen;Anton van den Hengel;Ian Reid

  • A Dilated Inception Network for Visual Saliency Prediction

    Sheng Yang;Guosheng Lin;Qiuping Jiang;Weisi Lin

  • MoNet: Deep Motion Exploitation for Video Object Segmentation

    Huaxin Xiao;Jiashi Feng;Guosheng Lin;Yu Liu

  • Multi-Path Region Mining for Weakly Supervised 3D Semantic Segmentation on Point Clouds

    Jiacheng Wei;Guosheng Lin;Kim-Hui Yap;Tzu-Yi Hung

  • Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

    Yukun Su;Ruizhou Sun;Guosheng Lin;Qingyao Wu

  • Learning Hash Functions Using Column Generation

    Xi Li;Guosheng Lin;Chunhua Shen;Anton Van den Hengel

  • RefineNet: Multi-Path Refinement Networks for Dense Prediction

    Guosheng Lin;Fayao Liu;Anton Milan;Chunhua Shen

  • Fast Training of Triplet-Based Deep Binary Embedding Networks

    Bohan Zhuang;Guosheng Lin;Chunhua Shen;Ian Reid

  • Learning Markov Clustering Networks for Scene Text Detection

    Zichuan Liu;Guosheng Lin;Sheng Yang;Jiashi Feng

Frequent Co-Authors

Chunhua Shen
Chunhua Shen Zhejiang University
Ian Reid
Ian Reid University of Adelaide
Anton van den Hengel
Anton van den Hengel University of Adelaide
Jianfei Cai
Jianfei Cai Monash University
Steven C. H. Hoi
Steven C. H. Hoi Alibaba Group (China)
Weisi Lin
Weisi Lin Nanyang Technological University
Lingqiao Liu
Lingqiao Liu University of Adelaide
Henghui Ding
Henghui Ding Nanyang Technological University
David Suter
David Suter Edith Cowan University
Chunyan Miao
Chunyan Miao Nanyang Technological University

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