Yuki M. Asano
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Environmental Engineering
- Global and Planetary Change
- Co-authors
- Andrea VedaldiHiroyuki KusakaDominik SachaFlorian MetzeMandela PatrickJürgen BernardMichael BehrischDaniel A. Keim
- Topics
- Domain Adaptation and Few-Shot Learning (7 papers)Meteorological Phenomena and Simulations (5 papers)Multimodal Machine Learning Applications (5 papers)
- Journals
- Proceedings of the National Academy of SciencesGeophysical Research LettersBuilding and Environment
- Partner nations
- JapanNetherlandsUnited Kingdom
In The Last Decade
Yuki M. Asano
28 papers receiving 318 citations
Peers
Comparison fields: 5 of 89
- Computer Vision and Pattern Recognition 156
- Artificial Intelligence 107
- Signal Processing 48
- Environmental Engineering 30
- Global and Planetary Change 24
Countries citing papers authored by Yuki M. Asano
This map shows the geographic impact of Yuki M. Asano'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 Yuki M. Asano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuki M. Asano more than expected).
Fields of papers citing papers by Yuki M. Asano
This network shows the impact of papers produced by Yuki M. Asano. 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 Yuki M. Asano. The network helps show where Yuki M. Asano may publish in the future.
Co-authorship network of co-authors of Yuki M. Asano
This figure shows the co-authorship network connecting the top 25 collaborators of Yuki M. Asano. A scholar is included among the top collaborators of Yuki M. Asano 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 Yuki M. Asano. Yuki M. Asano 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 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | 11 | |
| 10 | 2 | |
| 11 | 3 | |
| 12 | 23 | |
| 13 | 10 | |
| 14 | 11 | |
| 15 | 31 | |
| 16 | Labelling unlabelled videos from scratch with multi-modal self-supervision | 6 |
| 17 | A critical analysis of self-supervision, or what we can learn from a single image | 11 |
| 18 | Surprising Effectiveness of Few-Image Unsupervised Feature Learning. | 3 |
| 19 | Coordination of lexical and paralinguistic F0 in L2 production. | 1 |
| 20 | 1 |
About Yuki M. Asano
Yuki M. Asano is a scholar working on Computer Vision and Pattern Recognition, Environmental Engineering and Biophysics, having authored 33 papers that have together received 326 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (7 papers), Meteorological Phenomena and Simulations (5 papers) and Multimodal Machine Learning Applications (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (156 citations), Signal Processing (48 citations) and Artificial Intelligence (107 citations). Yuki M. Asano has collaborated with scholars based in Japan, Netherlands and United Kingdom. Frequent co-authors include Andrea Vedaldi, Hiroyuki Kusaka, Dominik Sacha, Florian Metze, Mandela Patrick, Jürgen Bernard, Michael Behrisch, Daniel A. Keim, Matthias Kraus and Tobias Schreck. Their work appears in journals such as Proceedings of the National Academy of Sciences, Geophysical Research Letters and Building and Environment.
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.