Mu Cai
Impact in
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- Advanced Image Processing Techniques
- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
- Image and Signal Denoising Methods
- Digital Media Forensic Detection
- Advanced Neural Network Applications
Papers in
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- Domain Adaptation and Few-Shot Learning 2
- Topic Modeling 2
- Natural Language Processing Techniques 2
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- Generative Adversarial Networks and Image Synthesis 2
- Multimodal Machine Learning Applications 2
- Co-authors
- Yixuan Li (2 shared papers)Qichuan Geng (1 shared paper)Gao Huang (1 shared paper)Hong Zhang (1 shared paper)Wei Zhan (1 shared paper)Liting Sun (1 shared paper)Masayoshi Tomizuka (1 shared paper)Andy Zhou (1 shared paper)
- Journals
- 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- United StatesChinaCanada
In The Last Decade
Mu Cai
9 papers receiving 119 citations
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 76
- Media Technology 12
- Artificial Intelligence 43
- Industrial and Manufacturing Engineering 6
- Automotive Engineering 7
Countries citing papers authored by Mu Cai
This map shows the geographic impact of Mu 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 Mu Cai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mu Cai more than expected).
Fields of papers citing papers by Mu Cai
This network shows the impact of papers produced by Mu 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 Mu Cai. The network helps show where Mu Cai may publish in the future.
Co-authors
The 22 scholars most cited alongside Mu Cai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 59 | |
| 2 | 2024 | 17 | |
| 3 | 2023 | 16 | |
| 4 | 2020 | 11 | |
| 5 | 2023 | 9 | |
| 6 | M-Elite Coevolutionary Algorithm for Numerical Optimization | 2009 | 6 |
| 7 | 2024 | 3 | |
| 8 | 2024 | 1 | |
| 9 | How the Shift Parameter Affects the Behavior of a Family of Meta-Fibonacci Sequences | 2008 | 1 |
| 10 | 2024 | 0 | |
| 11 | 2024 | 0 | |
| 12 | 2025 | 0 |
About Mu Cai
Mu Cai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Control and Systems Engineering and Information Systems, having authored 12 papers that have together received 123 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Topic Modeling (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Natural Language Processing Techniques (2 papers), Multimodal Machine Learning Applications (2 papers), 3D Surveying and Cultural Heritage (1 paper), Advanced Mathematical Theories and Applications (1 paper) and Mathematics, Computing, and Information Processing (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (76 citations), Media Technology (12 citations), Artificial Intelligence (43 citations), Industrial and Manufacturing Engineering (6 citations) and Automotive Engineering (7 citations). Mu Cai has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Yixuan Li, Qichuan Geng, Gao Huang, Hong Zhang, Wei Zhan, Liting Sun, Masayoshi Tomizuka, Andy Zhou, Haotian Liu and Haohan Wang. Their work appears in journals such as 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.