World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
18479
World Ranking
2314
National Ranking
317

Qingshan Liu 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 Qingshan Liu 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: 337 publications — 80th percentile

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

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

Qingshan Liu 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 Qingshan Liu 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: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Overview

Qingshan Liu is affiliated with Nanjing University of Information Science and Technology in China. Their research primarily spans the fields of Computer Science and Engineering, with a significant focus on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Atmospheric Science, and Computational Mechanics.

The scientist's work addresses a range of advanced topics including Advanced Neural Network Applications, Advanced Image and Video Retrieval Techniques, Remote-Sensing Image Classification, Visual Attention and Saliency Detection, Domain Adaptation and Few-Shot Learning, Human Pose and Action Recognition, and Advanced Vision and Imaging.

Qingshan Liu has published extensively with frequent appearances in several reputable venues. These include:

  • IEEE Transactions on Geoscience and Remote Sensing
  • arXiv (Cornell University)
  • IEEE Transactions on Image Processing
  • IEEE Transactions on Circuits and Systems for Video Technology
  • IEEE Transactions on Multimedia

The researcher has collaborated multiple times with several frequent co-authors such as Renlong Hang, Kaihua Zhang, Hui Shuai, Yubao Sun, and Guiyu Xia. This indicates a strong network of partnerships particularly focused on remote sensing and image analysis topics.

Recent notable papers authored or co-authored by Qingshan Liu include:

  • Classification of Hyperspectral and LiDAR Data Using Coupled CNNs (2020, IEEE Transactions on Geoscience and Remote Sensing)
  • Hyperspectral Image Classification With Attention-Aided CNNs (2020, IEEE Transactions on Geoscience and Remote Sensing)
  • Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild (2021, IEEE Transactions on Image Processing)
  • Robust Lightweight Facial Expression Recognition Network with Label Distribution Training (2021, Proceedings of the AAAI Conference on Artificial Intelligence)
  • Classification of Hyperspectral Images via Multitask Generative Adversarial Networks (2020, IEEE Transactions on Geoscience and Remote Sensing)

The focus of these publications highlights an emphasis on hyperspectral image analysis, machine learning methodologies such as convolutional neural networks and generative adversarial networks, and application domains including remote sensing and facial expression recognition.

Best Publications

  • Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

    Renlong Hang;Qingshan Liu;Danfeng Hong;Pedram Ghamisi

  • Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

    Jun Xu;Lei Xiang;Qingshan Liu;Hannah Gilmore

  • Fast Visual Tracking via Dense Spatio-temporal Context Learning

    Kaihua Zhang;Lei Zhang;Qingshan Liu;Dapeng Zhang

  • Face detection using improved LBP under Bayesian framework

    Hongliang Jin;Qingshan Liu;Hanqing Lu;Xiaofeng Tong

  • Learning active facial patches for expression analysis

    Lin Zhong;Qingshan Liu;Peng Yang;Bo Liu

  • A nonlinear approach for face sketch synthesis and recognition

    Qingshan Liu;Xiaoou Tang;Hongliang Jin;Hanqing Lu

  • Solving the small sample size problem of LDA

    Rui Huang;Qingshan Liu;Hanqing Lu;Songde Ma

  • Robust Visual Tracking via Convolutional Networks Without Training

    Kaihua Zhang;Qingshan Liu;Yi Wu;Ming-Hsuan Yang

  • Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

    Renlong Hang;Zhu Li;Pedram Ghamisi;Danfeng Hong

  • Image retrieval via probabilistic hypergraph ranking

    Yuchi Huang;Qingshan Liu;Shaoting Zhang;Dimitris N. Metaxas

  • Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks

    Huihui Song;Qingshan Liu;Guojie Wang;Renlong Hang

  • Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild

    Zengqun Zhao;Qingshan Liu;Shanmin Wang

  • Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

    Qingshan Liu;Feng Zhou;Renlong Hang;Xiaotong Yuan

  • Hyperspectral Image Classification With Attention-Aided CNNs

    Renlong Hang;Zhu Li;Qingshan Liu;Pedram Ghamisi

  • Stacked Hourglass Network for Robust Facial Landmark Localisation

    Jing Yang;Qingshan Liu;Kaihua Zhang

  • Abnormal detection using interaction energy potentials

    Xinyi Cui;Qingshan Liu;Mingchen Gao;Dimitris N. Metaxas

  • Improving kernel Fisher discriminant analysis for face recognition

    Qingshan Liu;Hanqing Lu;Songde Ma

  • Image annotation via graph learning

    Jing Liu;Mingjing Li;Qingshan Liu;Hanqing Lu

  • Video object segmentation by hypergraph cut

    Yuchi Huang;Qingshan Liu;Dimitris Metaxas

  • Hyperspectral image classification using spectral-spatial LSTMs

    Feng Zhou;Renlong Hang;Qingshan Liu;Xiaotong Yuan

  • Face recognition using kernel based fisher discriminant analysis

    Qingshan Liu;Rui Huang;Hanqing Lu;Songde Ma

Frequent Co-Authors

Hanqing Lu
Hanqing Lu Chinese Academy of Sciences
Dimitris N. Metaxas
Dimitris N. Metaxas Rutgers, The State University of New Jersey
Kaihua Zhang
Kaihua Zhang Fudan University
Jian Cheng
Jian Cheng Chinese Academy of Sciences
Guangcan Liu
Guangcan Liu Southeast University
Jing Liu
Jing Liu Chinese Academy of Sciences
Jiankang Deng
Jiankang Deng Imperial College London
Jinqiao Wang
Jinqiao Wang Chinese Academy of Sciences
Dacheng Tao
Dacheng Tao Nanyang Technological University
Junchi Yan
Junchi Yan Shanghai Jiao Tong University

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