Shunta Saito

1.2k citations
18 papers · 684 · h-index 9

Impact in

    • Remote-Sensing Image Classification
    • Generative Adversarial Networks and Image Synthesis
    • Advanced Vision and Imaging
    • Advanced Image Processing Techniques
    • Advanced Neural Network Applications
    • Human Pose and Action Recognition

Papers in

Shunta Saito

18 papers receiving 657 citations

Peers

Shunta Saito
Comparison fields: 5 of 88
  • Media Technology 179
  • Computer Vision and Pattern Recognition 413
  • Ocean Engineering 216
  • Environmental Engineering 180
  • Computer Graphics and Computer-Aided Design 30
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Citations per field
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Citations per year

Countries citing papers authored by Shunta Saito

Since Specialization
Citations

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

Fields of papers citing papers by Shunta Saito

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Shunta Saito, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Shunta Saito Line = papers co-authored together Shunta Saito links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2017203
2 2016148
3 201569
4 201968
5 201565
6 202057
7 201719
8
TGANv2: Efficient Training of Large Models for Video Generation with Multiple Subsampling Layers
201813
9 201512
10 20178
11 20127
12 20143
13 20123
14 20133
15 20112
16
Superpixel clustering with deep features for unsupervised road segmentation.
20172
17
An intelligent application development platform for service robots
20151
18 20111

About Shunta Saito

Shunta Saito is a scholar working on Computer Vision and Pattern Recognition, Ocean Engineering, Control and Systems Engineering, Computational Mechanics and Environmental Engineering, having authored 18 papers that have together received 684 indexed citations. Recurring topics across this work include Automated Road and Building Extraction (6 papers), Human Pose and Action Recognition (6 papers), 3D Shape Modeling and Analysis (4 papers), Remote Sensing and LiDAR Applications (4 papers), Advanced Neural Network Applications (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Human Motion and Animation (3 papers). The work is most often cited by research in Media Technology (179 citations), Computer Vision and Pattern Recognition (413 citations), Ocean Engineering (216 citations), Environmental Engineering (180 citations) and Computer Graphics and Computer-Aided Design (30 citations). Shunta Saito has collaborated with scholars based in Japan and United States. Frequent co-authors include Yoshimitsu Aoki, Masaki Saito, Takayoshi Yamashita, Eiichi Matsumoto, Sosuke Kobayashi, Masanori Koyama, Toru Ogawa, Takuya Akiba, Shuji Suzuki and Seiya Tokui. Their work appears in journals such as Journal of Imaging Science and Technology, International Journal of Computer Vision, IEEE Access, Footwear Science and arXiv (Cornell University).

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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