Yutaro Shigeto
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Radiology, Nuclear Medicine and Imaging
- Experimental and Cognitive Psychology
- Pulmonary and Respiratory Medicine
- Co-authors
- Akikazu TakeuchiYuji MatsumotoKeigo NakamuraKeisuke SakaguchiMasashi ShimboYūji MatsumotoShuhei KondoToshiyuki Maeda
- Topics
- Multimodal Machine Learning Applications (4 papers)Human Pose and Action Recognition (4 papers)Natural Language Processing Techniques (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceExperimental and Cognitive Psychology
- Journals
- IEEE Transactions on Circuits and Systems for Video TechnologyPattern Recognition LettersComputer Vision and Image Understanding
- Partner nations
- JapanTaiwanUnited States
In The Last Decade
Yutaro Shigeto
9 papers receiving 93 citations
Peers
Comparison fields: 5 of 24
- Computer Vision and Pattern Recognition 71
- Artificial Intelligence 67
- Radiology, Nuclear Medicine and Imaging 9
- Experimental and Cognitive Psychology 6
- Pulmonary and Respiratory Medicine 5
Countries citing papers authored by Yutaro Shigeto
This map shows the geographic impact of Yutaro Shigeto'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 Yutaro Shigeto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yutaro Shigeto more than expected).
Fields of papers citing papers by Yutaro Shigeto
This network shows the impact of papers produced by Yutaro Shigeto. 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 Yutaro Shigeto. The network helps show where Yutaro Shigeto may publish in the future.
Co-authorship network of co-authors of Yutaro Shigeto
This figure shows the co-authorship network connecting the top 25 collaborators of Yutaro Shigeto. A scholar is included among the top collaborators of Yutaro Shigeto 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 Yutaro Shigeto. Yutaro Shigeto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 15 | |
| 4 | 6 | |
| 5 | 3 | |
| 6 | Video Caption Dataset for Describing Human Actions in Japanese | 1 |
| 7 | 63 | |
| 8 | 0 | |
| 9 | Construction of English MWE Dictionary and its Application to POS Tagging | 7 |
| 10 | 1 |
About Yutaro Shigeto
Yutaro Shigeto is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 10 papers that have together received 100 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Human Pose and Action Recognition (4 papers) and Natural Language Processing Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (71 citations), Artificial Intelligence (67 citations) and Experimental and Cognitive Psychology (6 citations). Yutaro Shigeto has collaborated with scholars based in Japan, Taiwan and United States. Frequent co-authors include Akikazu Takeuchi, Yuji Matsumoto, Keigo Nakamura, Keisuke Sakaguchi, Masashi Shimbo, Yūji Matsumoto, Shuhei Kondo, Toshiyuki Maeda, Naoki Yamamoto and Takashi Izumi. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition Letters and Computer Vision and Image Understanding.
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.