Yutaro Shigeto

491 total citations
10 papers, 100 citations indexed

About

Yutaro Shigeto is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Yutaro Shigeto has authored 10 papers receiving a total of 100 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Yutaro Shigeto's work include Multimodal Machine Learning Applications (4 papers), Human Pose and Action Recognition (4 papers) and Natural Language Processing Techniques (3 papers). Yutaro Shigeto is often cited by papers focused on Multimodal Machine Learning Applications (4 papers), Human Pose and Action Recognition (4 papers) and Natural Language Processing Techniques (3 papers). Yutaro Shigeto collaborates with scholars based in Japan, Taiwan and United States. Yutaro Shigeto's co-authors include Akikazu Takeuchi, Keigo Nakamura, Yuji Matsumoto, Yūji Matsumoto, Shuhei Kondo, Keisuke Sakaguchi, Toshiyuki Maeda, Masashi Shimbo, Ikumi Suzuki and Kazuo Hara and has published in prestigious journals such as IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition Letters and Computer Vision and Image Understanding.

In The Last Decade

Yutaro Shigeto

9 papers receiving 93 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yutaro Shigeto Japan	4	71	67	9	6	5	10	100
Renrui Zhang China	3	57 0.8×	56 0.8×	12 1.3×	2 0.3×		5	95
Quinn Jones United States	3	47 0.7×	51 0.8×	10 1.1×	2 0.3×		4	80
Nikita Dvornik Canada	3	55 0.8×	27 0.4×	10 1.1×		2 0.4×	6	75
Junwen Pan China	4	46 0.6×	40 0.6×	15 1.7×	1 0.2×	2 0.4×	7	86
Taihong Xiao United States	4	91 1.3×	43 0.6×	4 0.4×	2 0.3×		6	118
Lewei Lu China	1	53 0.7×	43 0.6×	10 1.1×	2 0.3×		3	105
Rosanne Liu United States	3	82 1.2×	73 1.1×	13 1.4×	1 0.2×		5	121
Corentin Dancette France	4	51 0.7×	43 0.6×	7 0.8×			10	69
Quang Pham Singapore	5	27 0.4×	52 0.8×	6 0.7×	2 0.3×		10	69
Ardhendu Shekhar Tripathi Switzerland	4	50 0.7×	33 0.5×	12 1.3×		2 0.4×	4	58

Countries citing papers authored by Yutaro Shigeto

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Shigeto, Yutaro, et al.. (2023). Learning Decorrelated Representations Efficiently Using Fast Fourier Transform. 2052–2060. 2 indexed citations

2.

Shigeto, Yutaro, et al.. (2023). Action class relation detection and classification across multiple video datasets. Pattern Recognition Letters. 173. 93–100. 2 indexed citations

3.

Shigeto, Yutaro, et al.. (2021). Characterization of Pulmonary Nodules in Computed Tomography Images Based on Pseudo-Labeling Using Radiology Reports. IEEE Transactions on Circuits and Systems for Video Technology. 32(5). 2582–2591. 15 indexed citations

4.

Shigeto, Yutaro, et al.. (2021). MetaVD: A Meta Video Dataset for enhancing human action recognition datasets. Computer Vision and Image Understanding. 212. 103276–103276. 6 indexed citations

5.

Maeda, Toshiyuki, et al.. (2021). Predicting How CNN Training Time Changes on Various Mini-Batch Sizes by Considering Convolution Algorithms and Non-GPU Time. 11–18. 3 indexed citations

6.

Shigeto, Yutaro, et al.. (2020). Video Caption Dataset for Describing Human Actions in Japanese. arXiv (Cornell University). 4664–4670. 1 indexed citations

7.

Shigeto, Yutaro, et al.. (2017). STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset. 417–421. 63 indexed citations

8.

Shigeto, Yutaro, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, & Yūji Matsumoto. (2015). Reducing Hub Translation Candidates Improves the Accuracy of Bilingual Lexicon Extraction from Comparable Corpora. Transactions of the Japanese Society for Artificial Intelligence. 31(2). E–F43_1.

9.

Shigeto, Yutaro, et al.. (2013). Construction of English MWE Dictionary and its Application to POS Tagging. North American Chapter of the Association for Computational Linguistics. 139–144. 7 indexed citations

10.

Yamamoto, Naoki, et al.. (2012). Application Of Multi-Dimensional Principal Component Analysis To Medical Data. Zenodo (CERN European Organization for Nuclear Research). 6(3). 280–286. 1 indexed citations

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.

Explore authors with similar magnitude of impact