Fuwen Tan
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Computational Mechanics
- Aerospace Engineering
- Geology
- Co-authors
- Vicente OrdóñezSong FengBenjamin J. CohenConnelly BarnesPaola Cascante-BonillaHui HuangMinglun GongDaniel Cohen‐Or
- Topics
- Multimodal Machine Learning Applications (4 papers)Advanced Image and Video Retrieval Techniques (3 papers)Generative Adversarial Networks and Image Synthesis (2 papers)
- Journals
- ACM Transactions on GraphicsJMIR Formative Research2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Partner nations
- United StatesSingaporeHong Kong
In The Last Decade
Fuwen Tan
11 papers receiving 196 citations
Peers
Comparison fields: 5 of 37
- Computer Vision and Pattern Recognition 173
- Artificial Intelligence 47
- Computational Mechanics 28
- Aerospace Engineering 19
- Geology 18
Countries citing papers authored by Fuwen Tan
This map shows the geographic impact of Fuwen Tan'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 Fuwen Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fuwen Tan more than expected).
Fields of papers citing papers by Fuwen Tan
This network shows the impact of papers produced by Fuwen Tan. 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 Fuwen Tan. The network helps show where Fuwen Tan may publish in the future.
Co-authorship network of co-authors of Fuwen Tan
This figure shows the co-authorship network connecting the top 25 collaborators of Fuwen Tan. A scholar is included among the top collaborators of Fuwen Tan 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 Fuwen Tan. Fuwen Tan 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 | 62 | |
| 4 | Curriculum Labeling: Self-paced Pseudo-Labeling for Semi-Supervised Learning. | 9 |
| 5 | 8 | |
| 6 | 55 | |
| 7 | Text2Scene: Generating Abstract Scenes from Textual Descriptions. | 6 |
| 8 | 31 | |
| 9 | 4 | |
| 10 | 1 | |
| 11 | 22 |
About Fuwen Tan
Fuwen Tan is a scholar working on Computer Vision and Pattern Recognition, Geology and Artificial Intelligence, having authored 11 papers that have together received 201 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (173 citations), Computer Graphics and Computer-Aided Design (16 citations) and Geology (18 citations). Fuwen Tan has collaborated with scholars based in United States, Singapore and Hong Kong. Frequent co-authors include Vicente Ordóñez, Song Feng, Benjamin J. Cohen, Connelly Barnes, Paola Cascante-Bonilla, Hui Huang, Minglun Gong, Daniel Cohen‐Or, Hao Zhang and Yanjun Qi. Their work appears in journals such as ACM Transactions on Graphics, JMIR Formative Research and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.