Mingfu Yan
Impact in
-
- Advanced Image Processing Techniques
- Image Enhancement Techniques
- Generative Adversarial Networks and Image Synthesis
- Image and Signal Denoising Methods
- Advanced Image and Video Retrieval Techniques
- Advanced Vision and Imaging
- Medical Image Segmentation Techniques
- Video Analysis and Summarization
Papers in
-
- Generative Adversarial Networks and Image Synthesis 2
- Video Analysis and Summarization 1
- Multimodal Machine Learning Applications 1
- Image and Signal Denoising Methods 1
-
- Artificial Intelligence in Games 1
- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Shifeng Chen (5 shared papers)Jianzhuang Liu (3 shared papers)Chaoqi Chen (1 shared paper)Dong Yu (1 shared paper)He Zhang (1 shared paper)Liangliang Cao (1 shared paper)Xiaoxin Chen (1 shared paper)Shifeng Chen (1 shared paper)
- Journals
- IEEE Transactions on Multimedia (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Mingfu Yan
6 papers receiving 79 citations
Mingfu Yan's Hit Papers
Peers
Comparison fields: 5 of 23
- Computer Vision and Pattern Recognition 61
- Computer Graphics and Computer-Aided Design 7
- Media Technology 10
- Health Informatics 1
- Safety Research 4
Countries citing papers authored by Mingfu Yan
This map shows the geographic impact of Mingfu Yan'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 Mingfu Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingfu Yan more than expected).
Fields of papers citing papers by Mingfu Yan
This network shows the impact of papers produced by Mingfu Yan. 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 Mingfu Yan. The network helps show where Mingfu Yan may publish in the future.
Co-authors
The 9 scholars most cited alongside Mingfu Yan, 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 | 2024 | 41 | |
| 2 | Diffusion Model-Based Image Editing: A Survey Hit paper breakdown → | 2025 | 20 |
| 3 | 2024 | 11 | |
| 4 | 2024 | 5 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 1 |
About Mingfu Yan
Mingfu Yan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Computational Mechanics and Computational Theory and Mathematics, having authored 6 papers that have together received 81 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (2 papers), Artificial Intelligence in Games (1 paper), Video Analysis and Summarization (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Multimodal Machine Learning Applications (1 paper), Image and Signal Denoising Methods (1 paper), Human Motion and Animation (1 paper) and Martial Arts: Techniques, Psychology, and Education (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (61 citations), Computer Graphics and Computer-Aided Design (7 citations), Media Technology (10 citations), Health Informatics (1 citation) and Safety Research (4 citations). Mingfu Yan has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Shifeng Chen, Jianzhuang Liu, Chaoqi Chen, Dong Yu, He Zhang, Liangliang Cao, Xiaoxin Chen, Shifeng Chen and Yi Huang. Their work appears in journals such as IEEE Transactions on Multimedia 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.