Xiao-Ming Wu
Impact in
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- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Artificial Intelligence top 0.5%
- Advanced Graph Neural Networks
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Text and Document Classification Technologies
Papers in
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- Advanced Image and Video Retrieval Techniques 13
- Medical Image Segmentation Techniques 11
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- Topic Modeling 15
- Advanced Graph Neural Networks 12
- Domain Adaptation and Few-Shot Learning 10
Xiao-Ming Wu
152 papers receiving 3.8k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Computer Vision and Pattern Recognition 1.3k
- Artificial Intelligence 1.9k
- Statistical and Nonlinear Physics 429
- Media Technology 218
- Information Systems 520
Countries citing papers authored by Xiao-Ming Wu
This map shows the geographic impact of Xiao-Ming Wu'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 Xiao-Ming Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiao-Ming Wu more than expected).
Fields of papers citing papers by Xiao-Ming Wu
This network shows the impact of papers produced by Xiao-Ming Wu. 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 Xiao-Ming Wu. The network helps show where Xiao-Ming Wu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xiao-Ming Wu, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 6 | |
| 11 | 2024 | 4 | |
| 12 | 2024 | 3 | |
| 13 | 2024 | 2 | |
| 14 | 2024 | 6 | |
| 15 | ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models Hit paper breakdown → | 2024 | 46 |
| 16 | 2023 | 10 | |
| 17 | 2023 | 5 | |
| 18 | 2023 | 5 | |
| 19 | 2022 | 19 | |
| 20 | 2017 | 28 |
About Xiao-Ming Wu
Xiao-Ming Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Media Technology and Information Systems, having authored 165 papers that have together received 3.9k indexed citations. Recurring topics across this work include Topic Modeling (15 papers), Recommender Systems and Techniques (15 papers), Advanced Image and Video Retrieval Techniques (13 papers), Advanced Graph Neural Networks (12 papers), Energy Efficient Wireless Sensor Networks (12 papers), Medical Image Segmentation Techniques (11 papers), Domain Adaptation and Few-Shot Learning (10 papers) and EEG and Brain-Computer Interfaces (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.3k citations), Artificial Intelligence (1.9k citations), Statistical and Nonlinear Physics (429 citations), Media Technology (218 citations) and Information Systems (520 citations). Xiao-Ming Wu has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Qimai Li, Zhichao Han, Zhenguo Li, Shih‐Fu Chang, Li-Ming Zhan, Xiaotong Zhang, Lu Fan, Han Liu, Bo Liu and Beilei Fan. Their work appears in journals such as Food Control, Information Sciences, Engineering Applications of Artificial Intelligence, Pattern Recognition and IEEE Transactions on Knowledge and Data Engineering.
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.