Yoshitaka Ushiku

5.9k citations
55 papers · 1.2k indexed · 1 hit paper · h-index 17
Topics
Multimodal Machine Learning Applications (19 papers)Advanced Image and Video Retrieval Techniques (17 papers)Video Analysis and Summarization (10 papers)
Journals
SHILAP Revista de lepidopterologíaIEEE AccessFrontiers in Psychology

In The Last Decade

Yoshitaka Ushiku

47 papers receiving 1.2k citations

Hit Papers

MFNet: Towards real-time semantic segmentation for autono...20172026202020232017100200300

Peers

Yoshitaka Ushiku
Comparison fields: 5 of 133
  • Computer Vision and Pattern Recognition 813
  • Artificial Intelligence 243
  • Media Technology 238
  • Aerospace Engineering 170
  • Signal Processing 58
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Xuyu Xiang China
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Citations per field
00.5×7.5×
Xuyu Xiang · 1×
Citations per year

Countries citing papers authored by Yoshitaka Ushiku

Since Specialization
Citations

This map shows the geographic impact of Yoshitaka Ushiku'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 Yoshitaka Ushiku with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yoshitaka Ushiku more than expected).

Fields of papers citing papers by Yoshitaka Ushiku

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yoshitaka Ushiku. 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 Yoshitaka Ushiku. The network helps show where Yoshitaka Ushiku may publish in the future.

Co-authorship network of co-authors of Yoshitaka Ushiku

This figure shows the co-authorship network connecting the top 25 collaborators of Yoshitaka Ushiku. A scholar is included among the top collaborators of Yoshitaka Ushiku 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 Yoshitaka Ushiku. Yoshitaka Ushiku is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling
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3 0
4 10
5 2
6 0
7 0
8 1
9 2
10 4
11 5
12 18
13 7
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Visual Grounding Annotation of Recipe Flow Graph
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15 12
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Decentralized Learning of Generative Adversarial Networks from Multi-Client Non-iid Data.
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17
Learning from Between-class Examples for Deep Sound Recognition
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18 25
19 44
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ISI at ImageCLEF 2012: Scalable System for Image Annotation.
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About Yoshitaka Ushiku

Yoshitaka Ushiku is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction, having authored 55 papers that have together received 1.2k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (19 papers), Advanced Image and Video Retrieval Techniques (17 papers) and Video Analysis and Summarization (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (813 citations), Media Technology (238 citations) and Artificial Intelligence (243 citations). Yoshitaka Ushiku has collaborated with scholars based in Japan, United States and Switzerland. Frequent co-authors include Tatsuya Harada, Takumi Karasawa, Kohei Watanabe, Yasuo Kuniyoshi, Masataka Yamaguchi, Kuniaki Saito, Yutaka Maruyama, Ryo Igarashi, Ayori Mitsutake and Atsushi Hashimoto. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Frontiers in Psychology.

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.

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