Ziyun Cai

758 total citations
38 papers, 515 citations indexed

About

Ziyun Cai is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Ziyun Cai has authored 38 papers receiving a total of 515 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 21 papers in Artificial Intelligence and 5 papers in Cancer Research. Recurrent topics in Ziyun Cai's work include Domain Adaptation and Few-Shot Learning (16 papers), Multimodal Machine Learning Applications (13 papers) and Advanced Image Processing Techniques (7 papers). Ziyun Cai is often cited by papers focused on Domain Adaptation and Few-Shot Learning (16 papers), Multimodal Machine Learning Applications (13 papers) and Advanced Image Processing Techniques (7 papers). Ziyun Cai collaborates with scholars based in China, United Arab Emirates and United Kingdom. Ziyun Cai's co-authors include Ling Shao, Xiao‐Yuan Jing, Li Liu, Jungong Han, Li Liu, Xiaoke Zhu, Songcan Chen, Bo Du, Zhenyu He and Dong Yue and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and IEEE Transactions on Image Processing.

In The Last Decade

Ziyun Cai

33 papers receiving 507 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ziyun Cai China 11 336 172 84 46 34 38 515
Fangneng Zhan Singapore 17 787 2.3× 170 1.0× 91 1.1× 33 0.7× 25 0.7× 29 941
Anurag Arnab United States 12 669 2.0× 364 2.1× 46 0.5× 30 0.7× 28 0.8× 28 880
Renrui Zhang China 10 453 1.3× 312 1.8× 45 0.5× 50 1.1× 19 0.6× 18 732
Rongyao Fang Hong Kong 5 355 1.1× 247 1.4× 33 0.4× 39 0.8× 12 0.4× 6 542
Hanli Zhao China 16 355 1.1× 190 1.1× 79 0.9× 23 0.5× 15 0.4× 54 646
Feifei Lee Japan 15 399 1.2× 174 1.0× 72 0.9× 41 0.9× 16 0.5× 70 619
Yuanyuan Ding China 13 438 1.3× 209 1.2× 52 0.6× 31 0.7× 15 0.4× 35 671
Yunyang Xiong United States 8 249 0.7× 128 0.7× 40 0.5× 45 1.0× 48 1.4× 16 479
Alexander Vezhnevets Switzerland 8 739 2.2× 285 1.7× 100 1.2× 69 1.5× 17 0.5× 12 886
Linjun Sun China 14 338 1.0× 213 1.2× 59 0.7× 18 0.4× 17 0.5× 38 651

Countries citing papers authored by Ziyun Cai

Since Specialization
Citations

This map shows the geographic impact of Ziyun Cai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ziyun Cai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ziyun Cai more than expected).

Fields of papers citing papers by Ziyun Cai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ziyun Cai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ziyun Cai. The network helps show where Ziyun Cai may publish in the future.

Co-authorship network of co-authors of Ziyun Cai

This figure shows the co-authorship network connecting the top 25 collaborators of Ziyun Cai. A scholar is included among the top collaborators of Ziyun Cai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ziyun Cai. Ziyun Cai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cai, Ziyun, Yawen Huang, Changhui Hu, et al.. (2025). Make source-free transfer great: Dynamic uncertainty-driven confident fusion for multi-source-free domain adaptation. Information Fusion. 127. 103859–103859.
2.
Song, Jie, et al.. (2025). DUSA-UNet: Dual Sparse Attentive U-Net for Multiscale Road Network Extraction. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–14. 1 indexed citations
3.
Hu, Changhui, et al.. (2025). RestormGAN: Restormer with generative facial prior towards real-world blind face restoration. Computers & Electrical Engineering. 123. 110095–110095.
4.
Cai, Ziyun, Yawen Huang, Tengfei Zhang, Yefeng Zheng, & Yue Dong. (2025). Multi-Source Domain Adaptation by Causal-Guided Adaptive Multimodal Diffusion Networks. International Journal of Computer Vision. 133(7). 4623–4645. 2 indexed citations
5.
Chen, Linjun, et al.. (2025). Sample-pair learning network for extremely imbalanced classification. Neurocomputing. 634. 129859–129859.
6.
Hu, Changhui, et al.. (2024). Degrade for upgrade: Learning degradation representations for real-world low-light image enhancement. Computers & Electrical Engineering. 119. 109622–109622. 10 indexed citations
7.
Hu, Changhui, et al.. (2024). ZMAR-SNFlow:Restoration for low-light images with massive zero-element pixels. Computers & Electrical Engineering. 120. 109750–109750. 3 indexed citations
8.
Hu, Changhui, et al.. (2024). UPT-Flow: Multi-scale transformer-guided normalizing flow for low-light image enhancement. Pattern Recognition. 158. 111076–111076. 34 indexed citations
9.
Hu, Changhui, Yin Hu, Ziyun Cai, et al.. (2024). Multiscale hybrid feature guided normalizing flow for low-light image enhancement. Computers & Electrical Engineering. 122. 109922–109922. 1 indexed citations
10.
Cai, Ziyun, et al.. (2023). Single-/Multi-Source Domain Adaptation via domain separation: A simple but effective method. Pattern Recognition Letters. 174. 124–129. 8 indexed citations
11.
Cai, Ziyun, Jie Song, Tengfei Zhang, Changhui Hu, & Xiao‐Yuan Jing. (2023). Local weight coupled network: multi-modal unequal semi-supervised domain adaptation. Multimedia Tools and Applications. 83(2). 4331–4357. 3 indexed citations
12.
Cai, Ziyun, Yawen Huang, Tengfei Zhang, et al.. (2023). Attention Cycle-consistent universal network for More Universal Domain Adaptation. Pattern Recognition. 147. 110109–110109. 4 indexed citations
13.
Wang, Siyu, et al.. (2023). Flow Learning Based Dual Networks for Low-Light Image Enhancement. Neural Processing Letters. 55(6). 8115–8130. 3 indexed citations
14.
Min, Fan, Ziyun Cai, Tengfei Zhang, & Baoyun Wang. (2022). A survey of deep domain adaptation based on label set classification. Multimedia Tools and Applications. 81(27). 39545–39576. 7 indexed citations
15.
Cai, Ziyun, Tengfei Zhang, Fumin Ma, & Xiao‐Yuan Jing. (2022). Dual contrastive universal adaptation network for multi-source visual recognition. Knowledge-Based Systems. 254. 109632–109632. 8 indexed citations
16.
Min, Fan, Ziyun Cai, Tengfei Zhang, & Baoyun Wang. (2021). Adversarial Domain Adaptation via Class Correlation. 468–472.
17.
Cai, Ziyun, Jie Song, Tengfei Zhang, Xiao‐Yuan Jing, & Ling Shao. (2021). Dual Contrastive Universal Adaptation Network. 45. 1–6. 2 indexed citations
18.
Cai, Ziyun, Xiao‐Yuan Jing, & Ling Shao. (2020). Visual-Depth Matching Network: Deep RGB-D Domain Adaptation With Unequal Categories. IEEE Transactions on Cybernetics. 52(6). 4623–4635. 10 indexed citations
19.
Jing, Xiao‐Yuan, Xiaoke Zhu, Songcan Chen, et al.. (2020). Semi-Supervised Multi-View Deep Discriminant Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(7). 2496–2509. 125 indexed citations
20.
Cai, Ziyun, Yang Long, & Ling Shao. (2019). Classification complexity assessment for hyper-parameter optimization. Pattern Recognition Letters. 125. 396–403. 28 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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