Ming Shao
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 1%
- Signal Processing top 1%
- Cognitive Neuroscience top 10%
- Biomedical Engineering
- Topics
- Face recognition and analysis (26 papers)Face and Expression Recognition (26 papers)Domain Adaptation and Few-Shot Learning (19 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image ProcessingNanoscale
- Partner nations
- United StatesChinaMexico
In The Last Decade
Ming Shao
99 papers receiving 2.7k citations
Peers
Comparison fields: 5 of 139
- Computer Vision and Pattern Recognition 1.8k
- Artificial Intelligence 981
- Signal Processing 483
- Cognitive Neuroscience 241
- Biomedical Engineering 229
Countries citing papers authored by Ming Shao
This map shows the geographic impact of Ming Shao'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 Shao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Shao more than expected).
Fields of papers citing papers by Ming Shao
This network shows the impact of papers produced by Ming Shao. 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 Shao. The network helps show where Ming Shao may publish in the future.
Co-authorship network of co-authors of Ming Shao
This figure shows the co-authorship network connecting the top 25 collaborators of Ming Shao. A scholar is included among the top collaborators of Ming Shao 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 Ming Shao. Ming Shao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 6 | |
| 7 | 16 | |
| 8 | 0 | |
| 9 | Visual Kinship Recognition: A Decade in the Making | 2 |
| 10 | 107 | |
| 11 | Robust Knowledge Discovery via Low-rank Modeling. | 1 |
| 12 | Deep Evolutionary 3D Diffusion Heat Maps for Large-pose Face Alignment. | 4 |
| 13 | High-quality Ellipse Detection Based on Arc-support Line Segments. | 3 |
| 14 | 2 | |
| 15 | 19 | |
| 16 | Deep linear coding for fast graph clustering | 26 |
| 17 | Cross-view projective dictionary learning for person re-identification | 79 |
| 18 | Deep low-rank coding for transfer learning | 56 |
| 19 | 2 | |
| 20 | 2 |
About Ming Shao
Ming Shao is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 115 papers that have together received 2.7k indexed citations. Recurring topics across this work include Face recognition and analysis (26 papers), Face and Expression Recognition (26 papers) and Domain Adaptation and Few-Shot Learning (19 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.8k citations), Signal Processing (483 citations) and Human-Computer Interaction (186 citations). Ming Shao has collaborated with scholars based in United States, China and Mexico. Frequent co-authors include Yun Fu, Zhengming Ding, Siyu Xia, Sheng Li, Dmitry Kit, Hongfu Liu, Yun Fu, Jiebo Luo, Joseph P. Robinson and Changsheng Lu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Nanoscale.
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.