World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
76
Citations
22795
World Ranking
1344
National Ranking
705

Gang Hua 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 Gang Hua 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: 303 publications — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Gang Hua 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 Gang Hua 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: 76 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2019 - IEEE Fellow For contributions to facial recognition in images and videos
  • 2016 - ACM Distinguished Member

Overview

Gang Hua is affiliated with Dolby in the United States. Their research primarily spans the field of Computer Science with a concentration in Computer Vision and Pattern Recognition, having published extensively in this area.

The main subfields of study include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Automotive Engineering
  • Biomedical Engineering
  • Control and Systems Engineering

Key research topics covered in their work are:

  • Human Pose and Action Recognition
  • Anomaly Detection Techniques and Applications
  • Video Surveillance and Tracking Methods
  • Adversarial Robustness in Machine Learning
  • Advanced Neural Network Applications
  • Advanced Vision and Imaging
  • Domain Adaptation and Few-Shot Learning

Gang Hua has contributed to several recent papers, including:

  • Deep Model Intellectual Property Protection via Deep Watermarking, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Poison Ink: Robust and Invisible Backdoor Attack, 2022, IEEE Transactions on Image Processing
  • Efficient Semantic Image Synthesis via Class-Adaptive Normalization, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Memory-augmented appearance-motion network for video anomaly detection, 2023, Pattern Recognition
  • ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization, 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors collaborating with Gang Hua include:

  • Le Wang
  • Wei Tang
  • Sanping Zhou
  • Dongdong Chen
  • Nanning Zheng

The most common venues for their research publications are:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Transactions on Image Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Gang Hua has been recognized with several awards, including being named an IEEE Fellow in 2019 for contributions to facial recognition in images and videos, as well as being designated an ACM Distinguished Member in 2016.

Best Publications

  • A convolutional neural network cascade for face detection

    Haoxiang Li;Zhe Lin;Xiaohui Shen;Jonathan Brandt

  • Stacked Cross Attention for Image-Text Matching

    Kuang-Huei Lee;Xi Chen;Gang Hua;Houdong Hu

  • Gated Context Aggregation Network for Image Dehazing and Deraining

    Dongdong Chen;Mingming He;Qingnan Fan;Jing Liao

  • LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

    Dongqing Zhang;Jiaolong Yang;Dongqiangzi Ye;Gang Hua

  • Ordinal Regression with Multiple Output CNN for Age Estimation

    Zhenxing Niu;Mo Zhou;Le Wang;Xinbo Gao

  • Discriminative Learning of Local Image Descriptors

    M Brown;Gang Hua;S Winder

  • CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

    Jianmin Bao;Dong Chen;Fang Wen;Houqiang Li

  • Labeled Faces in the Wild: A Survey

    Erik Learned-Miller;Gary B. Huang;Aruni RoyChowdhury;Haoxiang Li

  • StyleBank: An Explicit Representation for Neural Image Style Transfer

    Dongdong Chen;Lu Yuan;Jing Liao;Nenghai Yu

  • Visual attribute transfer through deep image analogy

    Jing Liao;Yuan Yao;Lu Yuan;Gang Hua

  • Neural Aggregation Network for Video Face Recognition

    Jiaolong Yang;Peiran Ren;Dongqing Zhang;Dong Chen

  • Picking the best DAISY

    Simon Winder;Gang Hua;Matthew Brown

  • How to train a compact binary neural network with high accuracy

    Wei Tang;Gang Hua;Liang Wang

  • A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing

    Qingnan Fan;Jiaolong Yang;Gang Hua;Baoquan Chen

  • Context-Aware Visual Tracking

    Ming Yang;Ying Wu;Gang Hua

  • Coherent Online Video Style Transfer

    Dongdong Chen;Jing Liao;Lu Yuan;Nenghai Yu

  • Learning Discriminative Reconstructions for Unsupervised Outlier Removal

    Yan Xia;Xudong Cao;Fang Wen;Gang Hua

  • SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction

    Liushuai Shi;Le Wang;Chengjiang Long;Sanping Zhou

  • Descriptive visual words and visual phrases for image applications

    Shiliang Zhang;Qi Tian;Gang Hua;Qingming Huang

  • Probabilistic Elastic Matching for Pose Variant Face Verification

    Haoxiang Li;Gang Hua;Zhe Lin;Jonathan Brandt

  • Similarity learning on an explicit polynomial kernel feature map for person re-identification

    Dapeng Chen;Zejian Yuan;Gang Hua;Nanning Zheng

Frequent Co-Authors

Nanning Zheng
Nanning Zheng Xi'an Jiaotong University
Ying Wu
Ying Wu Northwestern University
Lu Yuan
Lu Yuan Microsoft (United States)
Zhengyou Zhang
Zhengyou Zhang Tencent (China)
Zicheng Liu
Zicheng Liu Microsoft (United States)
Qi Tian
Qi Tian Huawei Technologies (China)
Baoquan Chen
Baoquan Chen Peking University
Liangliang Cao
Liangliang Cao Google (United States)
David Wipf
David Wipf Amazon (United States)
Apostol Natsev
Apostol Natsev Google (United States)

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