Cun Mu
Impact in
- Computational Mathematics top 1%
- Tensor decomposition and applications
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- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Image Retrieval and Classification Techniques
- Multimodal Machine Learning Applications
- Video Analysis and Summarization
- Advanced Neural Network Applications
Papers in
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- Sparse and Compressive Sensing Techniques 3
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- Advanced Image and Video Retrieval Techniques 2
- Video Surveillance and Tracking Methods 1
- Face and Expression Recognition 1
- Co-authors
- Shih‐Fu Chang (2 shared papers)Wei Liu (2 shared papers)Sanjiv Kumar (1 shared paper)Donald Goldfarb (4 shared papers)John Wright (4 shared papers)Bo Huang (1 shared paper)Yuqian Zhang (2 shared papers)John Smith (1 shared paper)
- Journals
- SIAM Journal on Scientific Computing (1 paper)Computational Optimization and Applications (1 paper)SIAM Journal on Matrix Analysis and Applications (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesHong KongChina
In The Last Decade
Cun Mu
7 papers receiving 543 citations
Cun Mu's Hit Papers
Peers
Comparison fields: 5 of 38
- Computational Mathematics 126
- Computer Vision and Pattern Recognition 414
- Computational Mechanics 164
- Signal Processing 50
- Numerical Analysis 19
Countries citing papers authored by Cun Mu
This map shows the geographic impact of Cun Mu'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 Cun Mu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cun Mu more than expected).
Fields of papers citing papers by Cun Mu
This network shows the impact of papers produced by Cun Mu. 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 Cun Mu. The network helps show where Cun Mu may publish in the future.
Co-authors
The 14 scholars most cited alongside Cun Mu, 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 | Discrete Graph Hashing Hit paper breakdown → | 2014 | 331 |
| 2 | 2013 | 143 | |
| 3 | 2016 | 34 | |
| 4 | 2015 | 31 | |
| 5 | 2017 | 6 | |
| 6 | 2017 | 6 | |
| 7 | 2013 | 4 | |
| 8 | 2025 | 0 |
About Cun Mu
Cun Mu is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition, Computational Mathematics, Biomedical Engineering and Computer Networks and Communications, having authored 8 papers that have together received 555 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (3 papers), Tensor decomposition and applications (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Video Surveillance and Tracking Methods (1 paper), Caching and Content Delivery (1 paper), Spam and Phishing Detection (1 paper), Indoor and Outdoor Localization Technologies (1 paper) and Face and Expression Recognition (1 paper). The work is most often cited by research in Computational Mathematics (126 citations), Computer Vision and Pattern Recognition (414 citations), Computational Mechanics (164 citations), Signal Processing (50 citations) and Numerical Analysis (19 citations). Cun Mu has collaborated with scholars based in United States, Hong Kong and China. Frequent co-authors include Shih‐Fu Chang, Wei Liu, Sanjiv Kumar, Donald Goldfarb, John Wright, Bo Huang, Yuqian Zhang, John Smith, Shiqian Ma and Rongrong Ji. Their work appears in journals such as SIAM Journal on Scientific Computing, Computational Optimization and Applications, SIAM Journal on Matrix Analysis and Applications, arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.