Xuemeng Song
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 1%
- Information Systems top 1%
- Computational Mechanics top 5%
- Statistical and Nonlinear Physics top 5%
- Co-authors
- Liqiang NieTat‐Seng ChuaFuli FengZhiyong ChengXianjing HanLuming ZhangXin-Shun XuXiaojun Chang
- Topics
- Multimodal Machine Learning Applications (27 papers)Generative Adversarial Networks and Image Synthesis (24 papers)Advanced Image and Video Retrieval Techniques (22 papers)
In The Last Decade
Xuemeng Song
102 papers receiving 2.5k citations
Peers
Comparison fields: 5 of 109
- Computer Vision and Pattern Recognition 1.5k
- Artificial Intelligence 1.1k
- Information Systems 569
- Computational Mechanics 241
- Statistical and Nonlinear Physics 138
Countries citing papers authored by Xuemeng Song
This map shows the geographic impact of Xuemeng Song'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 Xuemeng Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xuemeng Song more than expected).
Fields of papers citing papers by Xuemeng Song
This network shows the impact of papers produced by Xuemeng Song. 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 Xuemeng Song. The network helps show where Xuemeng Song may publish in the future.
Co-authorship network of co-authors of Xuemeng Song
This figure shows the co-authorship network connecting the top 25 collaborators of Xuemeng Song. A scholar is included among the top collaborators of Xuemeng Song 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 Xuemeng Song. Xuemeng Song 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 | 0 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 2 | |
| 9 | 15 | |
| 10 | 9 | |
| 11 | 4 | |
| 12 | 10 | |
| 13 | 5 | |
| 14 | 34 | |
| 15 | 13 | |
| 16 | 7 | |
| 17 | 22 | |
| 18 | 109 | |
| 19 | Interest inference via structure-constrained multi-source multi-task learning | 54 |
| 20 | 54 |
About Xuemeng Song
Xuemeng Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems, having authored 115 papers that have together received 2.6k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (27 papers), Generative Adversarial Networks and Image Synthesis (24 papers) and Advanced Image and Video Retrieval Techniques (22 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.5k citations), Artificial Intelligence (1.1k citations) and Information Systems (569 citations). Xuemeng Song has collaborated with scholars based in China, Singapore and Australia. Frequent co-authors include Liqiang Nie, Tat‐Seng Chua, Fuli Feng, Zhiyong Cheng, Xianjing Han, Luming Zhang, Xin-Shun Xu, Xiaojun Chang, Haokun Wen and Jianlong Wu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Materials Science and Engineering A.
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.