Soravit Changpinyo
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Experimental and Cognitive Psychology
- Signal Processing
- Topics
- Multimodal Machine Learning Applications (12 papers)Advanced Image and Video Retrieval Techniques (7 papers)Domain Adaptation and Few-Shot Learning (7 papers)
- Journals
- International Journal of Computer VisionComputer Vision and Image Understanding2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Partner nations
- United StatesChina
In The Last Decade
Soravit Changpinyo
15 papers receiving 178 citations
Peers
Comparison fields: 5 of 38
- Computer Vision and Pattern Recognition 147
- Artificial Intelligence 123
- Radiology, Nuclear Medicine and Imaging 16
- Experimental and Cognitive Psychology 8
- Signal Processing 6
Countries citing papers authored by Soravit Changpinyo
This map shows the geographic impact of Soravit Changpinyo'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 Soravit Changpinyo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soravit Changpinyo more than expected).
Fields of papers citing papers by Soravit Changpinyo
This network shows the impact of papers produced by Soravit Changpinyo. 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 Soravit Changpinyo. The network helps show where Soravit Changpinyo may publish in the future.
Co-authorship network of co-authors of Soravit Changpinyo
This figure shows the co-authorship network connecting the top 25 collaborators of Soravit Changpinyo. A scholar is included among the top collaborators of Soravit Changpinyo 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 Soravit Changpinyo. Soravit Changpinyo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 8 | |
| 3 | 10 | |
| 4 | 0 | |
| 5 | 16 | |
| 6 | 7 | |
| 7 | 25 | |
| 8 | 1 | |
| 9 | 13 | |
| 10 | Robust Visual Reasoning via Language Guided Neural Module Networks | 5 |
| 11 | 13 | |
| 12 | 17 | |
| 13 | Weakly Supervised Content Selection for Improved Image Captioning | 1 |
| 14 | 39 | |
| 15 | 7 | |
| 16 | Similarity Component Analysis | 17 |
About Soravit Changpinyo
Soravit Changpinyo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Language and Linguistics, having authored 16 papers that have together received 183 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (12 papers), Advanced Image and Video Retrieval Techniques (7 papers) and Domain Adaptation and Few-Shot Learning (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (147 citations), Artificial Intelligence (123 citations) and Computational Mathematics (1 citation). Soravit Changpinyo has collaborated with scholars based in United States and China. Frequent co-authors include Fei Sha, Radu Soricut, Boqing Gong, Wei‐Lun Chao, Kuan Liu, Idan Szpektor, Hexiang Hu, Arjun Akula, Jordi Pont-Tuset and Vittorio Ferrari. Their work appears in journals such as International Journal of Computer Vision, Computer Vision and Image Understanding 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.