World's Best Scientists 2026 revealed!
Liang-Chieh Chen

Liang-Chieh Chen

D-Index & Metrics

Computer Science

D-Index
42
Citations
97571
World Ranking
8126
National Ranking
1071

Liang-Chieh Chen 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 Liang-Chieh Chen 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: 65 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

Liang-Chieh Chen 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 Liang-Chieh Chen 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Liang-Chieh Chen is affiliated with ByteDance in China and has a substantial publication record primarily focused on computer science. Their research is concentrated in the subfields of Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Radiology, Nuclear Medicine and Imaging, and Biomedical Engineering.

The scientist has published extensively, with frequent contributions to venues such as arXiv (Cornell University), where they have authored 31 papers. Additionally, they have published work in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and the International Journal of Computer Vision, as well as a contribution to TIB Data Manager.

Key research topics covered in their work include:

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

Recent publications by Liang-Chieh Chen include:

  • DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution, 2020, arXiv (Cornell University)
  • CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation, 2020, arXiv (Cornell University)
  • DeepLab2: A TensorFlow Library for Deep Labeling, 2021, arXiv (Cornell University)
  • MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers, 2024, TIB Data Manager

The scientist frequently collaborates with other researchers. Their most common co-authors are Qihang Yu, Hartwig Adam, Alan Yuille, Huiyu Wang, and Siyuan Qiao.

Best Publications

  • MobileNetV2: Inverted Residuals and Linear Bottlenecks

    Mark Sandler;Andrew Howard;Menglong Zhu;Andrey Zhmoginov

  • DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

    Liang-Chieh Chen;George Papandreou;Iasonas Kokkinos;Kevin Murphy

  • Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

    Liang-Chieh Chen;Yukun Zhu;George Papandreou;Florian Schroff

  • Rethinking Atrous Convolution for Semantic Image Segmentation

    Liang-Chieh Chen;George Papandreou;Florian Schroff;Hartwig Adam

  • Searching for MobileNetV3

    Andrew Howard;Ruoming Pang;Hartwig Adam;Quoc Le

  • Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

    Liang-Chieh Chen;George Papandreou;Iasonas Kokkinos;Kevin Murphy

  • Attention to Scale: Scale-Aware Semantic Image Segmentation

    Liang-Chieh Chen;Yi Yang;Jiang Wang;Wei Xu

  • Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

    George Papandreou;Liang-Chieh Chen;Kevin P. Murphy;Alan L. Yuille

  • Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation

    Chenxi Liu;Liang-Chieh Chen;Florian Schroff;Hartwig Adam

  • DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

    Siyuan Qiao;Liang-Chieh Chen;Alan Yuille

  • Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

    Andrew Howard;Andrey Zhmoginov;Liang-Chieh Chen;Mark Sandler

  • Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation

    Huiyu Wang;Yukun Zhu;Bradley Green;Hartwig Adam

  • Searching for MobileNetV3.

    Andrew Howard;Mark Sandler;Grace Chu;Liang-Chieh Chen

  • Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation

    Bowen Cheng;Maxwell D. Collins;Yukun Zhu;Ting Liu

  • PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model

    George Papandreou;Tyler Zhu;Liang-Chieh Chen;Spyros Gidaris

  • MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers

    Huiyu Wang;Yukun Zhu;Hartwig Adam;Alan Yuille

  • Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

    George Papandreou;Liang-Chieh Chen;Kevin Murphy;Alan L. Yuille

  • FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation

    Paul Voigtlaender;Yuning Chai;Florian Schroff;Hartwig Adam

  • MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

    Liang-Chieh Chen;Alexander Hermans;George Papandreou;Florian Schroff

  • Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform

    Liang-Chieh Chen;Jonathan T. Barron;George Papandreou;Kevin Murphy

Frequent Co-Authors

Hartwig Adam
Hartwig Adam Google (United States)
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Jonathon Shlens
Jonathon Shlens Google (United States)
Barret Zoph
Barret Zoph Google (United States)
Mark Sandler
Mark Sandler Google (United States)
Peng Wang
Peng Wang Baidu (China)
Jasper Uijlings
Jasper Uijlings Google (United States)
Ekin D. Cubuk
Ekin D. Cubuk Google (United States)
Bastian Leibe
Bastian Leibe RWTH Aachen University
Raquel Urtasun
Raquel Urtasun University of Toronto

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