Mingfei Gao
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Electrical and Electronic Engineering
- Computational Mechanics top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Larry S. DavisVlad I. MorariuRuichi YuChun-Fu ChenXintong HanChing‐Yung LinAng LiJui-Hsin Lai
- Topics
- Multimodal Machine Learning Applications (6 papers)Cognitive Radio Networks and Spectrum Sensing (5 papers)Human Pose and Action Recognition (5 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Mingfei Gao
28 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 786
- Artificial Intelligence 539
- Electrical and Electronic Engineering 141
- Computational Mechanics 71
- Computer Networks and Communications 69
Countries citing papers authored by Mingfei Gao
This map shows the geographic impact of Mingfei Gao'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 Mingfei Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingfei Gao more than expected).
Fields of papers citing papers by Mingfei Gao
This network shows the impact of papers produced by Mingfei Gao. 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 Mingfei Gao. The network helps show where Mingfei Gao may publish in the future.
Co-authorship network of co-authors of Mingfei Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Mingfei Gao. A scholar is included among the top collaborators of Mingfei Gao 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 Mingfei Gao. Mingfei Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 20 | |
| 4 | 40 | |
| 5 | 18 | |
| 6 | 24 | |
| 7 | 28 | |
| 8 | 50 | |
| 9 | 92 | |
| 10 | 21 | |
| 11 | 23 | |
| 12 | 45 | |
| 13 | 20 | |
| 14 | 95 | |
| 15 | NISP: Pruning Networks Using Neuron Importance Score Propagationbreakdown → | 470 |
| 16 | 4 | |
| 17 | 5 | |
| 18 | 3 | |
| 19 | 2 | |
| 20 | 8 |
About Mingfei Gao
Mingfei Gao is a scholar working on Energy Engineering and Power Technology, Computer Vision and Pattern Recognition and Media Technology, having authored 28 papers that have together received 1.2k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (6 papers), Cognitive Radio Networks and Spectrum Sensing (5 papers) and Human Pose and Action Recognition (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (786 citations), Artificial Intelligence (539 citations) and Computational Mathematics (7 citations). Mingfei Gao has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Larry S. Davis, Vlad I. Morariu, Ruichi Yu, Chun-Fu Chen, Xintong Han, Ching‐Yung Lin, Ang Li, Jui-Hsin Lai, Caiming Xiong and Mingze Xu. Their work appears in journals such as Energy, Journal of Energy Storage and Journal of Microscopy.
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.