World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
77
Citations
20022
World Ranking
1294
National Ranking
173

Zheng-Jun Zha 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 Zheng-Jun Zha 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: 416 publications — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Zheng-Jun Zha 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 Zheng-Jun Zha 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: 77 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.

Overview

Zheng-Jun Zha is affiliated with the University of Science and Technology of China in China. Their research primarily spans the field of Computer Science, with a substantial focus on Computer Vision and Pattern Recognition. Their work also touches upon Artificial Intelligence, Media Technology, Electrical and Electronic Engineering, and Biomedical Engineering.

The scientist has contributed to multiple domains including Multimodal Machine Learning Applications, Human Pose and Action Recognition, Image Enhancement Techniques, Advanced Image Processing Techniques, Domain Adaptation and Few-Shot Learning, Video Surveillance and Tracking Methods, and Advanced Neural Network Applications.

Frequent collaborators in Zheng-Jun Zha's work include Xueyang Fu, Liang Li, Kecheng Zheng, Wei Zhai, and Yang Cao. This network of coauthors indicates significant collaborative engagement within the research community.

Their publication record spans various reputable venues, notably:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Selected recent papers authored or coauthored by Zheng-Jun Zha include:

  • "Image De-Raining Transformer," published in 2022 in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Towards a new generation of artificial intelligence in China," published in 2020 in Nature Machine Intelligence
  • "Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization," published in 2020 in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification," published in 2021 in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning," published in 2022 in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Best Publications

  • Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search

    Yue Gao;Meng Wang;Zheng-Jun Zha;Jialie Shen

  • Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition

    Heliang Zheng;Jianlong Fu;Zheng-Jun Zha;Jiebo Luo

  • Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews

    Jianxing Yu;Zheng-Jun Zha;Meng Wang;Tat-Seng Chua

  • Object Relational Graph With Teacher-Recommended Learning for Video Captioning

    Ziqi Zhang;Yaya Shi;Chunfeng Yuan;Bing Li

  • Image De-Raining Transformer

    Unknown

  • Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification

    Meng Wang;R. Hong;Guangda Li;Zheng-Jun Zha

  • Adaptive Transfer Network for Cross-Domain Person Re-Identification

    Jiawei Liu;Zheng-Jun Zha;Di Chen;Richang Hong

  • Joint multi-label multi-instance learning for image classification

    Zheng-Jun Zha;Xian-Sheng Hua;Tao Mei;Jingdong Wang

  • Mining Travel Patterns from Geotagged Photos

    Yan-Tao Zheng;Zheng-Jun Zha;Tat-Seng Chua

  • Learning to Assemble Neural Module Tree Networks for Visual Grounding

    Daqing Liu;Hanwang Zhang;Zheng-Jun Zha;Feng Wu

  • Towards a new generation of artificial intelligence in China

    Fei Wu;Cewu Lu;Mingjie Zhu;Hao Chen

  • ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection

    Yuxin Wang;Hongtao Xie;Zheng-Jun Zha;Mengting Xing

  • MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition

    Yizhou Zhou;Xiaoyan Sun;Zheng-Jun Zha;Wenjun Zeng

  • Graph-based semi-supervised learning with multiple labels

    Zheng-Jun Zha;Tao Mei;Jingdong Wang;Zengfu Wang

  • Less is More: Efficient 3-D Object Retrieval With Query View Selection

    Yue Gao;Meng Wang;Zheng-Jun Zha;Qi Tian

  • Visual query suggestion

    Zheng-Jun Zha;Linjun Yang;Tao Mei;Meng Wang

  • Group-aware Label Transfer for Domain Adaptive Person Re-identification

    Kecheng Zheng;Wu Liu;Lingxiao He;Tao Mei

  • Parsing-Based View-Aware Embedding Network for Vehicle Re-Identification

    Dechao Meng;Liang Li;Xuejing Liu;Yadong Li

  • Multi-Scale Triplet CNN for Person Re-Identification

    Jiawei Liu;Zheng-Jun Zha;Qi Tian;Dong Liu

  • A Fast Uyghur Text Detector for Complex Background Images

    Chenggang Yan;Hongtao Xie;Jianjun Chen;Zhengjun Zha

  • Product Aspect Ranking and Its Applications

    Zheng-Jun Zha;Jianxing Yu;Jinhui Tang;Meng Wang

  • Attribute-augmented semantic hierarchy: towards bridging semantic gap and intention gap in image retrieval

    Hanwang Zhang;Zheng-Jun Zha;Yang Yang;Shuicheng Yan

  • Learning to Assemble Neural Module Tree Networks for Visual Grounding.

    Daqing Liu;Hanwang Zhang;Feng Wu;Zheng-Jun Zha

Frequent Co-Authors

Meng Wang
Meng Wang Hefei University of Technology
Tat-Seng Chua
Tat-Seng Chua National University of Singapore
Feng Wu
Feng Wu University of Science and Technology of China
Dong Liu
Dong Liu University of Science and Technology of China
Yongdong Zhang
Yongdong Zhang University of Science and Technology of China
Hanwang Zhang
Hanwang Zhang Nanyang Technological University
Richang Hong
Richang Hong Hefei University of Technology
Qingming Huang
Qingming Huang University of Chinese Academy of Sciences
Tao Mei
Tao Mei Jingdong (China)
Shuhui Wang
Shuhui Wang Chinese Academy of Sciences

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