World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
62
Citations
14277
World Ranking
2927
National Ranking
13

Tae-Kyun Kim 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 Tae-Kyun Kim 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: 270 publications — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Tae-Kyun Kim 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 Tae-Kyun Kim 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: 62 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Overview

Tae-Kyun Kim is affiliated with the Korea Advanced Institute of Science and Technology in South Korea and has contributed extensively to research in computer science and engineering. Their work spans several subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Aerospace Engineering, and Economics and Econometrics.

Their research covers a variety of main topics such as:

  • Human Pose and Action Recognition
  • Generative Adversarial Networks and Image Synthesis
  • Advanced Image Processing Techniques
  • Robot Manipulation and Learning
  • Domain Adaptation and Few-Shot Learning
  • Advanced Vision and Imaging
  • Advanced Neural Network Applications

Frequent publication venues for their work include:

  • arXiv (Cornell University)
  • Korean Journal of Agricultural Management and Policy
  • Academy of Management Proceedings
  • 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Journal of Agriculture & Life Science

Multiple collaborations have shaped their research output, frequently working with:

  • Binod Bhattarai
  • Wenhan Luo
  • Lin Wang
  • Guillermo Garcia-Hernando
  • Wonjoon Kim

Selected recent papers include:

  • Multiple Object Tracking: A Literature Review, 2020, Artificial Intelligence
  • Geometry-based Distance Decomposition for Monocular 3D Object Detection, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • A Review on Object Pose Recovery: From 3D Bounding Box Detectors to Full 6D Pose Estimators, 2020, Image and Vision Computing
  • When Does AI Pay Off? AI-adoption Intensity, Complementary Investments, and R&D Strategy, 2022, Technovation
  • GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions, 2024, International Journal of Computer Vision

Their body of work evidences a focus on advancing techniques in computer vision, AI adoption strategies, and complex image restoration methodologies. The cross-disciplinary approach involving economics and engineering aspects demonstrates a breadth of research interests aligned with technological and practical challenges.

Best Publications

  • Multiple object tracking: A literature review

    Wenhan Luo;Wenhan Luo;Junliang Xing;Anton Milan;Xiaoqin Zhang

  • Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

    Tae-Kyun Kim;J. Kittler;R. Cipolla

  • First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations

    Guillermo Garcia-Hernando;Shanxin Yuan;Seungryul Baek;Tae-Kyun Kim

  • Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

    Danhang Tang;Hyung Jin Chang;Alykhan Tejani;Tae-Kyun Kim

  • BOP: Benchmark for 6D Object Pose Estimation

    Tomas Hodan;Frank Michel;Eric Brachmann;Wadim Kehl

  • Tensor Canonical Correlation Analysis for Action Classification

    Tae-Kyun Kim;Shu-Fai Wong;R. Cipolla

  • Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image

    Tae-Kyun Kim;J. Kittler

  • Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection

    Tae-Kyun Kim;R. Cipolla

  • Latent-Class Hough Forests for 3D Object Detection and Pose Estimation

    Alykhan Tejani;Danhang Tang;Rigas Kouskouridas;Tae-Kyun Kim

  • Learning Motion Categories using both Semantic and Structural Information

    Shu-Fai Wong;Tae-Kyun Kim;R. Cipolla

  • Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests

    Danhang Tang;Tsz-Ho Yu;Tae-Kyun Kim

  • BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis

    Shanxin Yuan;Qi Ye;Bjorn Stenger;Siddhant Jain

  • Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd

    Andreas Doumanoglou;Rigas Kouskouridas;Sotiris Malassiotis;Tae-Kyun Kim

  • Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals

    Shanxin Yuan;Guillermo Garcia-Hernando;Bjorn Stenger;Gyeongsik Moon

  • Multiple Object Tracking: A Literature Review

    Wenhan Luo;Junliang Xing;Anton Milan;Xiaoqin Zhang

  • Pushing the Envelope for RGB-Based Dense 3D Hand Pose Estimation via Neural Rendering

    Seungryul Baek;Kwang In Kim;Tae-Kyun Kim

  • Real-time Action Recognition by Spatiotemporal Semantic and Structural Forests

    Tsz-Ho Yu;Tae-Kyun Kim;Roberto Cipolla

  • Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation

    Qi Ye;Shanxin Yuan;Tae-Kyun Kim

  • Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose

    Danhang Tang;Jonathan Taylor;Pushmeet Kohli;Cem Keskin

  • Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning

    Andreas Doumanoglou;Andreas Kargakos;Tae-Kyun Kim;Sotiris Malassiotis

  • 3D Hand Pose Estimation: From Current Achievements to Future Goals

    Shanxin Yuan;Guillermo Garcia-Hernando;Björn Stenger;Gyeongsik Moon

Frequent Co-Authors

Roberto Cipolla
Roberto Cipolla University of Cambridge
Bjorn Stenger
Bjorn Stenger Rakuten (Japan)
Josef Kittler
Josef Kittler University of Surrey
Hyung Jin Chang
Hyung Jin Chang University of Birmingham
Wenhan Luo
Wenhan Luo Hong Kong University of Science and Technology
Vincent Lepetit
Vincent Lepetit École des Ponts ParisTech
Junsong Yuan
Junsong Yuan University at Buffalo, State University of New York
Dongheui Lee
Dongheui Lee Technical University of Munich
Antonis A. Argyros
Antonis A. Argyros University of Crete
Andrew J. Davison
Andrew J. Davison Imperial College London

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