World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
19924
World Ranking
4443
National Ranking
598

Chang Huang 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 Chang Huang 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: 134 publications — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Chang Huang 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 Chang Huang 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: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Chang Huang is affiliated with Horizon Robotics Inc. in China and has contributed significantly to both Environmental Science and Computer Science. Their research spans a range of interdisciplinary topics, with a focus on the application of advanced computational methods within environmental and vision-related domains.

Their work addresses main fields of study including:

  • Environmental Science
  • Computer Science

Within these broad areas, Chang Huang's subfields of research include:

  • Computer Vision and Pattern Recognition
  • Global and Planetary Change
  • Atmospheric Science
  • Ecology
  • Water Science and Technology

The main research topics covered in their publications are:

  • Flood Risk Assessment and Management
  • Hydrology and Watershed Management Studies
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Land Use and Ecosystem Services
  • Video Surveillance and Tracking Methods
  • Domain Adaptation and Few-Shot Learning

Chang Huang has published papers in a variety of venues, with frequent contributions to:

  • arXiv (Cornell University)
  • Remote Sensing
  • Water
  • SSRN Electronic Journal
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors in their collaborative work include:

  • Xinggang Wang (21 publications)
  • Wenyu Liu (19 publications)
  • Shaoyu Chen (15 publications)
  • Tianheng Cheng (12 publications)
  • Qian Zhang (11 publications)

Recent representative papers by Chang Huang comprise:

  • Do major customers encourage innovative sustainable development? Empirical evidence from corporate green innovation in China, 2022, Business Strategy and the Environment
  • Unsupervised domain adaptive re-identification: Theory and practice, 2020, Pattern Recognition
  • Sparse Instance Activation for Real-Time Instance Segmentation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Diversity Transfer Network for Few-Shot Learning, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction, 2022, arXiv (Cornell University)

Best Publications

  • Conditional Random Fields as Recurrent Neural Networks

    Shuai Zheng;Sadeep Jayasumana;Bernardino Romera-Paredes;Vibhav Vineet

  • CCNet: Criss-Cross Attention for Semantic Segmentation

    Zilong Huang;Xinggang Wang;Lichao Huang;Chang Huang

  • The Visual Object Tracking VOT2017 Challenge Results

    Matej Kristan;Ales Leonardis;Jiri Matas;Michael Felsberg

  • The Visual Object Tracking VOT2015 Challenge Results

    Matej Kristan;Jiri Matas;Ale Leonardis;Michael Felsberg

  • CNN-RNN: A Unified Framework for Multi-label Image Classification

    Jiang Wang;Yi Yang;Junhua Mao;Zhiheng Huang

  • Mask Scoring R-CNN

    Zhaojin Huang;Lichao Huang;Yongchao Gong;Chang Huang

  • Learning from massive noisy labeled data for image classification

    Tong Xiao;Tian Xia;Yi Yang;Chang Huang

  • Learning to associate: HybridBoosted multi-target tracker for crowded scene

    Yuan Li;Chang Huang;Ram Nevatia

  • Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

    Chang Huang;Yun Chen;Shiqiang Zhang;Jianping Wu

  • Robust Object Tracking by Hierarchical Association of Detection Responses

    Chang Huang;Bo Wu;Ramakant Nevatia

  • Fast rotation invariant multi-view face detection based on real Adaboost

    Bo Wu;Haizhou Ai;Chang Huang;Shihong Lao

  • High-Performance Rotation Invariant Multiview Face Detection

    Chang Huang;Haizhou Ai;Yuan Li;Shihong Lao

  • Deep multiple instance learning for image classification and auto-annotation

    Jiajun Wu;Yinan Yu;Chang Huang;Kai Yu

  • Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks

    Chunshui Cao;Xianming Liu;Yi Yang;Yinan Yu

  • Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification

    Cheng Wang;Qian Zhang;Chang Huang;Wenyu Liu

  • Multi-target tracking by on-line learned discriminative appearance models

    Cheng-Hao Kuo;Chang Huang;Ramakant Nevatia

  • Unsupervised Domain Adaptive Re-Identification: Theory and Practice

    Liangchen Song;Cheng Wang;Lefei Zhang;Bo Du

  • Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas

    Kaifang Shi;Chang Huang;Bailang Yu;Bing Yin

  • Beyond spatial pyramids: Receptive field learning for pooled image features

    Yangqing Jia;Chang Huang;Trevor Darrell

  • Vector boosting for rotation invariant multi-view face detection

    Chang Huang;Haizhou Ai;Yuan Li;Shihong Lao

  • Targeting Ultimate Accuracy: Face Recognition via Deep Embedding

    Jingtuo Liu;Yafeng Deng;Tao Bai;Chang Huang

  • Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data

    Kaifang Shi;Kaifang Shi;Yun Chen;Bailang Yu;Tingbao Xu

  • Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China

    Bailang Yu;Kaifang Shi;Yingjie Hu;Chang Huang

  • Text Flow: A Unified Text Detection System in Natural Scene Images

    Shangxuan Tian;Yifeng Pan;Chang Huang;Shijian Lu

  • Sparse Instance Activation for Real-Time Instance Segmentation

    Unknown

Frequent Co-Authors

Haizhou Ai
Haizhou Ai Tsinghua University
Xinggang Wang
Xinggang Wang Huazhong University of Science and Technology
Shihong Lao
Shihong Lao SenseTime
Wenyu Liu
Wenyu Liu Huazhong University of Science and Technology
Shiming Xiang
Shiming Xiang Chinese Academy of Sciences
Gaofeng Meng
Gaofeng Meng Chinese Academy of Sciences
Junliang Xing
Junliang Xing Tsinghua University
Wei Xu
Wei Xu Horizon Robotics Inc.
Ramakant Nevatia
Ramakant Nevatia University of Southern California
Kai Yu
Kai Yu Horizon Robotics Inc.

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