Yuta Nakashima
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Signal Processing top 5%
- Ophthalmology top 5%
- Co-authors
- Hajime NagaharaRyo KawasakiLiangzhi LiManisha VermaMayu OtaniNoa GarcíaNoboru BabaguchiEsa Rahtu
- Topics
- Multimodal Machine Learning Applications (25 papers)Advanced Image and Video Retrieval Techniques (19 papers)Video Analysis and Summarization (14 papers)
- Partner nations
- JapanUnited StatesChina
In The Last Decade
Yuta Nakashima
99 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Computer Vision and Pattern Recognition 755
- Artificial Intelligence 273
- Radiology, Nuclear Medicine and Imaging 222
- Signal Processing 182
- Ophthalmology 125
Countries citing papers authored by Yuta Nakashima
This map shows the geographic impact of Yuta Nakashima'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 Yuta Nakashima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuta Nakashima more than expected).
Fields of papers citing papers by Yuta Nakashima
This network shows the impact of papers produced by Yuta Nakashima. 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 Yuta Nakashima. The network helps show where Yuta Nakashima may publish in the future.
Co-authorship network of co-authors of Yuta Nakashima
This figure shows the co-authorship network connecting the top 25 collaborators of Yuta Nakashima. A scholar is included among the top collaborators of Yuta Nakashima 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 Yuta Nakashima. Yuta Nakashima is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 4 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 5 | |
| 10 | 6 | |
| 11 | 2 | |
| 12 | 1 | |
| 13 | 31 | |
| 14 | 3 | |
| 15 | Analysis and Classification of Gestures in TED Talks | 0 |
| 16 | 42 | |
| 17 | 15 | |
| 18 | Summarization of user-generated sports video by using deep action recognition features | 70 |
| 19 | iParaphrasing: Extracting Visually Grounded Paraphrases via an Image | 3 |
| 20 | Quantitative evaluation on effectiveness of privacy protection for facial images | 2 |
About Yuta Nakashima
Yuta Nakashima is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Signal Processing, having authored 109 papers that have together received 1.1k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (25 papers), Advanced Image and Video Retrieval Techniques (19 papers) and Video Analysis and Summarization (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (755 citations), Signal Processing (182 citations) and Ophthalmology (125 citations). Yuta Nakashima has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Hajime Nagahara, Ryo Kawasaki, Liangzhi Li, Manisha Verma, Mayu Otani, Noa García, Noboru Babaguchi, Esa Rahtu, Naokazu Yokoya and Tomokazu Sato. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and Organic Letters.
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.