Soshi Shimada
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 10%
- Computational Mechanics
- Biomedical Engineering
- Human-Computer Interaction top 10%
- Co-authors
- Vladislav GolyanikChristian TheobaltWeipeng XuMarc HabermannXinyu YiFeng XuYuxiao ZhouPatrick Pérez
- Topics
- Human Pose and Action Recognition (7 papers)Advanced Vision and Imaging (5 papers)Human Motion and Animation (5 papers)
- Cited by
- Computer Vision and Pattern RecognitionHuman-Computer InteractionComputer Graphics and Computer-Aided Design
- Journals
- ACM Transactions on GraphicsJournal of Physics Conference Series2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
In The Last Decade
Soshi Shimada
13 papers receiving 325 citations
Peers
Comparison fields: 5 of 50
- Computer Vision and Pattern Recognition 261
- Control and Systems Engineering 130
- Computational Mechanics 63
- Biomedical Engineering 57
- Human-Computer Interaction 49
Countries citing papers authored by Soshi Shimada
This map shows the geographic impact of Soshi Shimada'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 Soshi Shimada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soshi Shimada more than expected).
Fields of papers citing papers by Soshi Shimada
This network shows the impact of papers produced by Soshi Shimada. 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 Soshi Shimada. The network helps show where Soshi Shimada may publish in the future.
Co-authorship network of co-authors of Soshi Shimada
This figure shows the co-authorship network connecting the top 25 collaborators of Soshi Shimada. A scholar is included among the top collaborators of Soshi Shimada 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 Soshi Shimada. Soshi Shimada is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 5 | |
| 3 | 9 | |
| 4 | 116 | |
| 5 | 10 | |
| 6 | 59 | |
| 7 | 3 | |
| 8 | 104 | |
| 9 | 1 | |
| 10 | 12 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | 8 | |
| 14 | 0 |
About Soshi Shimada
Soshi Shimada is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 14 papers that have together received 334 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (7 papers), Advanced Vision and Imaging (5 papers) and Human Motion and Animation (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (261 citations), Human-Computer Interaction (49 citations) and Computer Graphics and Computer-Aided Design (24 citations). Soshi Shimada has collaborated with scholars based in Germany, Japan and France. Frequent co-authors include Vladislav Golyanik, Christian Theobalt, Weipeng Xu, Marc Habermann, Xinyu Yi, Feng Xu, Yuxiao Zhou, Patrick Pérez, Zhi Li and Edgar Tretschk. Their work appears in journals such as ACM Transactions on Graphics, Journal of Physics Conference Series and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.