World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
19785
World Ranking
6986
National Ranking
934

Gong Cheng 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 Gong Cheng 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 87 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Gong Cheng 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 Gong Cheng sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Gong Cheng is affiliated with Northwestern Polytechnical University in China. Their research primarily focuses on computer science and engineering, with a particular emphasis on computer vision and pattern recognition, artificial intelligence, media technology, aerospace engineering, and electrical and electronic engineering.

The scientist's work covers various topics related to advanced neural network applications, advanced image and video retrieval techniques, remote-sensing image classification, domain adaptation and few-shot learning, adversarial robustness in machine learning, video surveillance and tracking methods, and infrared target detection methodologies.

Gong Cheng has published extensively in several venues, including:

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

Notable recent papers include:

  • Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities, 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Towards Large-Scale Small Object Detection: Survey and Benchmarks, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Anchor-Free Oriented Proposal Generator for Object Detection, 2022, IEEE Transactions on Geoscience and Remote Sensing
  • Weakly Supervised Object Localization and Detection: A Survey, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Learning What Not to Segment: A New Perspective on Few-Shot Segmentation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent collaborators in Gong Cheng's research include Junwei Han, Xiwen Yao, Chunbo Lang, Xingxing Xie, and Xiaoxu Feng. These co-authors have co-contributed to multiple publications, indicating established research partnerships.

Best Publications

  • Remote Sensing Image Scene Classification: Benchmark and State of the Art

    Gong Cheng;Junwei Han;Xiaoqiang Lu

  • Object detection in optical remote sensing images: A survey and a new benchmark

    Ke Li;Gang Wan;Gong Cheng;Liqiu Meng

  • Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images

    Gong Cheng;Peicheng Zhou;Junwei Han

  • A survey on object detection in optical remote sensing images

    Gong Cheng;Junwei Han

  • When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs

    Gong Cheng;Ceyuan Yang;Xiwen Yao;Lei Guo

  • Multi-class geospatial object detection and geographic image classification based on collection of part detectors

    Gong Cheng;Junwei Han;Peicheng Zhou;Lei Guo

  • Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

    Gong Cheng;Xingxing Xie;Junwei Han;Lei Guo

  • Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning

    Junwei Han;Dingwen Zhang;Gong Cheng;Lei Guo

  • Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey

    Junwei Han;Dingwen Zhang;Gong Cheng;Nian Liu

  • Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images

    Ke Li;Gong Cheng;Shuhui Bu;Xiong You

  • Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection

    Gong Cheng;Junwei Han;Peicheng Zhou;Dong Xu

  • Anchor-free Oriented Proposal Generator for Object Detection

    Gong Cheng;Jiabao Wang;Ke Li;Xingxing Xie

  • Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning

    Xiwen Yao;Junwei Han;Gong Cheng;Xueming Qian

  • Remote Sensing Image Scene Classification Using Bag of Convolutional Features

    Gong Cheng;Zhenpeng Li;Xiwen Yao;Lei Guo

  • Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images

    Gong Cheng;Junwei Han;Lei Guo;Zhenbao Liu

  • Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

    Gong Cheng;Lei Guo;Tianyun Zhao;Junwei Han

  • Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification

    Gong Cheng;Zhenpeng Li;Junwei Han;Xiwen Yao

  • Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification

    Peicheng Zhou;Junwei Han;Gong Cheng;Baochang Zhang

  • Weakly Supervised Object Localization and Detection: A Survey.

    Dingwen Zhang;Junwei Han;Gong Cheng;Ming Hsuan Yang

  • Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding

    Junwei Han;Peicheng Zhou;Dingwen Zhang;Gong Cheng

Frequent Co-Authors

Junwei Han
Junwei Han Northwestern Polytechnical University
Lei Guo
Lei Guo Beijing University of Posts and Telecommunications
Xintao Hu
Xintao Hu Northwestern Polytechnical University
Tianming Liu
Tianming Liu University of Georgia
Dong Xu
Dong Xu University of Hong Kong
Ming-Hsuan Yang
Ming-Hsuan Yang University of California, Merced
Jinchang Ren
Jinchang Ren Robert Gordon University
Xiaoqiang Lu
Xiaoqiang Lu Chinese Academy of Sciences
Jungong Han
Jungong Han Aberystwyth University
Yang Liu
Yang Liu Linköping University

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