Ming-Yu Liu
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Digital Media Forensic Detection
- Face recognition and analysis
- Image and Signal Denoising Methods
- Advanced Vision and Imaging
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- Computer Graphics and Visualization Techniques
Papers in
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- Multimodal Machine Learning Applications 1
- Image and Signal Denoising Methods 1
- Generative Adversarial Networks and Image Synthesis 1
- Human Pose and Action Recognition 1
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- Domain Adaptation and Few-Shot Learning 1
- Speech Recognition and Synthesis 1
- Co-authors
- Ting-Chun Wang (2 shared papers)Arun Mallya (2 shared papers)Xun Huang (1 shared paper)Jiahui Yu (1 shared paper)Jan Kautz (1 shared paper)Zhiding Yu (1 shared paper)Ayşegül Dündar (1 shared paper)Rafael Valle (1 shared paper)
- Journals
- Proceedings of the IEEE (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)
- Partner nations
- United States
In The Last Decade
Ming-Yu Liu
5 papers receiving 149 citations
Peers
Comparison fields: 5 of 53
- Computer Vision and Pattern Recognition 107
- Computer Graphics and Computer-Aided Design 14
- Health Informatics 2
- Signal Processing 16
- Artificial Intelligence 47
Countries citing papers authored by Ming-Yu Liu
This map shows the geographic impact of Ming-Yu Liu'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 Ming-Yu Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming-Yu Liu more than expected).
Fields of papers citing papers by Ming-Yu Liu
This network shows the impact of papers produced by Ming-Yu Liu. 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 Ming-Yu Liu. The network helps show where Ming-Yu Liu may publish in the future.
Co-authors
The 20 scholars most cited alongside Ming-Yu Liu, 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 | 130 | |
| 2 | 2020 | 22 | |
| 3 | 2024 | 3 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 1 |
About Ming-Yu Liu
Ming-Yu Liu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Geology and Infectious Diseases, having authored 5 papers that have together received 157 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (1 paper), Multimodal Machine Learning Applications (1 paper), Speech Recognition and Synthesis (1 paper), Image and Signal Denoising Methods (1 paper), Music and Audio Processing (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Speech and Audio Processing (1 paper) and Human Pose and Action Recognition (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (107 citations), Computer Graphics and Computer-Aided Design (14 citations), Health Informatics (2 citations), Signal Processing (16 citations) and Artificial Intelligence (47 citations). Ming-Yu Liu has collaborated with scholars based in United States. Frequent co-authors include Ting-Chun Wang, Arun Mallya, Xun Huang, Jiahui Yu, Jan Kautz, Zhiding Yu, Ayşegül Dündar, Rafael Valle, Zekun Hao and Hanzi Mao. Their work appears in journals such as Proceedings of the IEEE and IEEE Transactions on Pattern Analysis and Machine 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.