World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
22277
World Ranking
3540
National Ranking
20

Bohyung 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 Bohyung 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: 170 publications — 35th percentile

35% of scientists in this discipline score the same or lower.

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

Bohyung 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 Bohyung 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: 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

Bohyung Han is affiliated with Seoul National University in South Korea and is an active researcher in the field of Computer Science. Their work has a focus on several subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Computational Mechanics, and Civil and Structural Engineering.

Their research topics cover a range of areas primarily centered on Machine Learning and Computer Vision techniques. Notable topics include:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Advanced Vision and Imaging
  • Advanced Image Processing Techniques
  • Advanced Neural Network Applications
  • Anomaly Detection Techniques and Applications

Bohyung Han has contributed extensively to various publication venues. The most frequent venues where their research appears are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • SSRN Electronic Journal
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Recent selected papers authored by Bohyung Han demonstrate a focus on video frame interpolation, incremental learning, and neural rendering. These include:

  • Channel Attention Is All You Need for Video Frame Interpolation, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Class-Incremental Learning for Action Recognition in Videos, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Learning Student-Friendly Teacher Networks for Knowledge Distillation, 2021, arXiv (Cornell University)

Their research collaborations involve several frequent coauthors, reflecting interdisciplinary and project-based teamwork. The prominent coauthors working with Bohyung Han include:

  • Seonguk Seo
  • Jaeyoo Park
  • Minsoo Kang
  • Joon-Young Lee
  • Minji Kim

Best Publications

  • Learning Deconvolution Network for Semantic Segmentation

    Hyeonwoo Noh;Seunghoon Hong;Bohyung Han

  • Learning Multi-domain Convolutional Neural Networks for Visual Tracking

    Hyeonseob Nam;Bohyung Han

  • The Visual Object Tracking VOT2016 Challenge Results

    Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg

  • Large-Scale Image Retrieval with Attentive Deep Local Features

    Hyeonwoo Noh;Andre Araujo;Jack Sim;Tobias Weyand

  • Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network

    Seunghoon Hong;Tackgeun You;Suha Kwak;Bohyung Han

  • Domain-Specific Batch Normalization for Unsupervised Domain Adaptation

    Woong-Gi Chang;Tackgeun You;Seonguk Seo;Suha Kwak

  • Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction

    Hyeonwoo Noh;Paul Hongsuck Seo;Bohyung Han

  • Weakly Supervised Action Localization by Sparse Temporal Pooling Network

    Phuc Nguyen;Bohyung Han;Ting Liu;Gautam Prasad

  • The Visual Object Tracking VOT2014 challenge results

    Matej Kristan;Roman P. Pflugfelder;Ales Leonardis;Jiri Matas

  • Modeling and Propagating CNNs in a Tree Structure for Visual Tracking.

    Hyeonseob Nam;Mooyeol Baek;Bohyung Han

  • Decoupled deep neural network for semi-supervised semantic segmentation

    Seunghoon Hong;Hyeonwoo Noh;Bohyung Han

  • Channel Attention Is All You Need for Video Frame Interpolation

    Myungsub Choi;Heewon Kim;Bohyung Han;Ning Xu

  • Real-Time MDNet

    Ilchae Jung;Jeany Son;Mooyeol Baek;Bohyung Han

  • Local-Global Video-Text Interactions for Temporal Grounding

    Jonghwan Mun;Minsu Cho;Bohyung Han

  • Multi-object Tracking with Quadruplet Convolutional Neural Networks

    Jeany Son;Mooyeol Baek;Minsu Cho;Bohyung Han

  • Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation

    Unknown

  • Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking

    Bohyung Han;D. Comaniciu;Ying Zhu;L.S. Davis

  • InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering

    Unknown

  • Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

    Seunghoon Hong;Seunghoon Hong;Junhyuk Oh;Honglak Lee;Bohyung Han

  • Learning to Optimize Domain Specific Normalization for Domain Generalization

    Seonguk Seo;Yumin Suh;Dongwan Kim;Geeho Kim

  • Density-Based Multifeature Background Subtraction with Support Vector Machine

    Bohyung Han;L. S. Davis

  • Weakly Supervised Action Localization by Sparse Temporal Pooling Network

    Phuc Nguyen;Ting Liu;Gautam Prasad;Bohyung Han

  • Learning to Optimize Domain Specific Normalization for Domain Generalization

    Seonguk Seo;Yumin Suh;Dongwan Kim;Jongwoo Han

Frequent Co-Authors

Suha Kwak
Suha Kwak Korea Foundation for Max Planck POSTECH
Larry S. Davis
Larry S. Davis University of Maryland, College Park
Minsu Cho
Minsu Cho Pohang University of Science and Technology
Kyoung Mu Lee
Kyoung Mu Lee Seoul National University
Qingming Huang
Qingming Huang University of Chinese Academy of Sciences
Fatih Porikli
Fatih Porikli Australian National University
Matej Kristan
Matej Kristan University of Ljubljana
Michael Felsberg
Michael Felsberg Linköping University
Longyin Wen
Longyin Wen ByteDance

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 further education online can open up a wide range of career opportunities in computer science and related fields. Many students start with online associate degree programs, providing a solid foundation and entry into IT roles or further undergraduate study.

For those seeking to quickly advance their qualifications, there are options for the quickest masters degree online. These accelerated programs allow students to earn their credentials faster and enter the workforce or advance their careers sooner.

Choosing on-demand programs is crucial; pursuing the best masters degree to get can lead to roles in artificial intelligence, data science, or cybersecurity—all areas with strong job prospects and growth.

Not all valuable credentials require a full degree. Earning certifications that pay well is a strategic way to boost your resume and qualify for specialized positions, even in a short timeframe.

Whether you choose an associate degree, certification, or a graduate program, online pathways offer the flexibility and focus needed to build a successful career in computer science.

Best Scientists Citing Bohyung Han

Trending Scientists

Recently Published Articles