World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
18150
World Ranking
4208
National Ranking
124

Chang Xu 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 Xu 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: 206 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Chang Xu 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 Xu 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: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Overview

Chang Xu is a researcher affiliated with the University of Sydney in Australia, with a focus on the field of computer science and its related subfields. Their body of work centers around areas such as computer vision, artificial intelligence, and electrical and electronic engineering, contributing extensively to academic literature in these domains.

Their main fields of study include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Media Technology
  • Signal Processing

Chang Xu's research also spans several specialized topics that relate to contemporary challenges and technologies in machine learning and image processing. Prominent themes in their work include:

  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Adversarial Robustness in Machine Learning
  • Generative Adversarial Networks and Image Synthesis
  • Multimodal Machine Learning Applications
  • Anomaly Detection Techniques and Applications

Their recent publications highlight contributions to the development of neural architectures and applications in computer vision. Notable papers include:

  • "CMT: Convolutional Neural Networks Meet Vision Transformers" (2022), published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "GhostNetV2: Enhance Cheap Operation with Long-Range Attention" (2022), published on arXiv (Cornell University)
  • "SimMatch: Semi-supervised Learning with Similarity Matching" (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Vision transformer-based autonomous crack detection on asphalt and concrete surfaces" (2022), published in Automation in Construction
  • "GhostNets on Heterogeneous Devices via Cheap Operations" (2022), published in the International Journal of Computer Vision

Chang Xu frequently publishes in venues that are prominent in the field of artificial intelligence and computer vision, including:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • SSRN Electronic Journal

The researcher has collaborated extensively with other scholars. Frequent co-authors include:

  • Yunhe Wang
  • Shan You
  • Yehui Tang
  • Chunjing Xu
  • Minjing Dong

Best Publications

  • GhostNet: More Features From Cheap Operations

    Kai Han;Yunhe Wang;Qi Tian;Jianyuan Guo

  • Pre-Trained Image Processing Transformer

    Hanting Chen;Yunhe Wang;Tianyu Guo;Chang Xu

  • A Survey on Multi-view Learning

    Chang Xu;Dacheng Tao;Chao Xu

  • STRIP: a defence against trojan attacks on deep neural networks

    Yansong Gao;Change Xu;Derui Wang;Shiping Chen

  • Multi-View Intact Space Learning

    Chang Xu;Dacheng Tao;Chao Xu

  • Perceptual Adversarial Networks for Image-to-Image Transformation.

    Chaoyue Wang;Chang Xu;Chaohui Wang;Dacheng Tao

  • Learning from Multiple Teacher Networks

    Shan You;Chang Xu;Chao Xu;Dacheng Tao

  • Data-Free Learning of Student Networks

    Hanting Chen;Yunhe Wang;Chang Xu;Zhaohui Yang

  • Evolutionary Generative Adversarial Networks

    Chaoyue Wang;Chang Xu;Xin Yao;Dacheng Tao

  • Multi-Task Pose-Invariant Face Recognition

    Changxing Ding;Chang Xu;Dacheng Tao

  • CARS: Continuous Evolution for Efficient Neural Architecture Search

    Zhaohui Yang;Yunhe Wang;Xinghao Chen;Boxin Shi

  • Large-Margin Multi-ViewInformation Bottleneck

    Chang Xu;Dacheng Tao;Chao Xu

  • Distilling Object Detectors via Decoupled Features

    Jianyuan Guo;Kai Han;Yunhe Wang;Han Wu

  • Multi-View Learning With Incomplete Views

    Chang Xu;Dacheng Tao;Chao Xu

  • Self-Supervised Representation Learning by Rotation Feature Decoupling

    Zeyu Feng;Chang Xu;Dacheng Tao

  • Graph Edge Convolutional Neural Networks for Skeleton-Based Action Recognition

    Xikun Zhang;Chang Xu;Xinmei Tian;Dacheng Tao

  • Attention-GAN for Object Transfiguration in Wild Images

    Xinyuan Chen;Chang Xu;Xiaokang Yang;Dacheng Tao

  • AdderNet: Do We Really Need Multiplications in Deep Learning?

    Hanting Chen;Yunhe Wang;Chunjing Xu;Boxin Shi

  • Context Aware Graph Convolution for Skeleton-Based Action Recognition

    Xikun Zhang;Chang Xu;Dacheng Tao

  • CNNpack: packing convolutional neural networks in the frequency domain

    Yunhe Wang;Chang Xu;Shan You;Dacheng Tao

  • Uncovering collusive spammers in Chinese review websites

    Chang Xu;Jie Zhang;Kuiyu Chang;Chong Long

  • Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification

    Yong Luo;Dacheng Tao;Chang Xu;Chao Xu

Frequent Co-Authors

Dacheng Tao
Dacheng Tao Nanyang Technological University
Yunhe Wang
Yunhe Wang Huawei Technologies (China)
Chunjing Xu
Chunjing Xu Huawei Technologies (China)
Chao Xu
Chao Xu Peking University
Kai Han
Kai Han Huawei Technologies (China)
Boxin Shi
Boxin Shi Peking University
Changshui Zhang
Changshui Zhang Tsinghua University
Qi Tian
Qi Tian Huawei Technologies (China)
Surya Nepal
Surya Nepal Commonwealth Scientific and Industrial Research Organisation
Bo Du
Bo Du Wuhan University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Choosing to study Computer Science opens doors to a diverse array of career opportunities, both in the tech industry and beyond. For those seeking affordable education, there are many cheap online colleges offering flexible computer science programs tailored for different budgets.

If your academic history includes a lower GPA, don’t let this discourage you. There are reputable online graduate programs that accept 2.0 gpa, making advanced study accessible to more students.

Many students wonder how their degree can translate into real-world jobs. Besides traditional tech roles, your training may qualify you for careers at the intersection of technology and other fields. For example, see what can you do with an environmental science major to understand cross-disciplinary paths that might inspire your journey.

For those eager to jumpstart their future, consider a faster pace with an accelerated computer science degree. These programs allow you to complete your studies in less time and enter the workforce sooner.

Best Scientists Citing Chang Xu

Trending Scientists

Recently Published Articles