Tetsuya Ogata

6.9k total citations · 1 hit paper
378 papers, 4.4k citations indexed

About

Tetsuya Ogata is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Tetsuya Ogata has authored 378 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 124 papers in Control and Systems Engineering, 123 papers in Computer Vision and Pattern Recognition and 122 papers in Artificial Intelligence. Recurrent topics in Tetsuya Ogata's work include Robot Manipulation and Learning (101 papers), Speech and Audio Processing (84 papers) and Music and Audio Processing (74 papers). Tetsuya Ogata is often cited by papers focused on Robot Manipulation and Learning (101 papers), Speech and Audio Processing (84 papers) and Music and Audio Processing (74 papers). Tetsuya Ogata collaborates with scholars based in Japan, United Kingdom and United States. Tetsuya Ogata's co-authors include Hiroshi G. Okuno, Kazunori Komatani, Kazuhiro Nakadai, Shigeki Sugano, Kuniaki Noda, Yuki Yamaguchi, Masataka Goto, Jun Tani, Hiroaki Arie and Kazuyoshi Yoshii and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Communications of the ACM.

In The Last Decade

Tetsuya Ogata

349 papers receiving 4.2k citations

Hit Papers

Audio-visual speech recognition using deep learning 2014 2026 2018 2022 2014 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Audio-visual speech recognition using deep learning
    2014 404 citations Kuniaki Noda, Hiroshi G. Okuno et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tetsuya Ogata Japan 30 1.4k 1.4k 1.2k 1.1k 650 378 4.4k
Stefan Wermter Germany 32 1.7k 1.2× 438 0.3× 2.9k 2.4× 616 0.5× 792 1.2× 299 5.5k
Sérgio Escalera Spain 41 3.6k 2.5× 1.0k 0.8× 1.5k 1.2× 369 0.3× 393 0.6× 261 6.4k
Sanghoon Lee South Korea 42 3.5k 2.4× 387 0.3× 510 0.4× 612 0.5× 417 0.6× 552 8.5k
Hideyuki Takagi Japan 23 1.1k 0.7× 512 0.4× 1.5k 1.3× 396 0.4× 230 0.4× 131 3.1k
Tingfan Wu China 24 1.1k 0.8× 285 0.2× 1.4k 1.2× 248 0.2× 315 0.5× 56 4.3k
Jia Pan Hong Kong 36 2.9k 2.0× 169 0.1× 1.0k 0.9× 1.8k 1.6× 443 0.7× 239 5.8k
Fuji Ren Japan 37 1.3k 0.9× 430 0.3× 2.7k 2.3× 132 0.1× 384 0.6× 537 5.7k
Jie Yang China 22 1.5k 1.1× 378 0.3× 511 0.4× 236 0.2× 437 0.7× 152 3.3k
Guodong Guo China 54 7.7k 5.4× 2.4k 1.8× 1.6k 1.4× 249 0.2× 403 0.6× 235 10.1k
Weihong Deng China 41 7.0k 4.9× 1.9k 1.4× 2.3k 2.0× 170 0.2× 516 0.8× 174 9.6k

Countries citing papers authored by Tetsuya Ogata

Since Specialization
Citations

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

Fields of papers citing papers by Tetsuya Ogata

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tetsuya Ogata

This figure shows the co-authorship network connecting the top 25 collaborators of Tetsuya Ogata. A scholar is included among the top collaborators of Tetsuya Ogata 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 Tetsuya Ogata. Tetsuya Ogata 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
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MORI, Hiroki, et al.. (2024). Augmenting Compliance With Motion Generation Through Imitation Learning Using Drop-Stitch Reinforced Inflatable Robot Arm With Rigid Joints. IEEE Robotics and Automation Letters. 9(10). 8595–8602. 4 indexed citations
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Schmitz, Alexander, et al.. (2022). Multi-Fingered In-Hand Manipulation With Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors. IEEE Robotics and Automation Letters. 7(2). 2102–2109. 31 indexed citations
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Yan, Gang, et al.. (2022). Tactile Transfer Learning and Object Recognition With a Multifingered Hand Using Morphology Specific Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 35(6). 7587–7601. 15 indexed citations
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Ito, Hiroshi, et al.. (2021). Object Grasping Motion Generation by Attention Prediction Based on Language Instructions. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2021(0). 1P3–D05.
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Sadakari, Yoshihiko, et al.. (2021). The use of ultrasound in central vascular ligation during laparoscopic right-sided colon cancer surgery: technical notes. Techniques in Coloproctology. 25(10). 1155–1161. 1 indexed citations
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Suzuki, Kanata, et al.. (2020). Effective Imitation Learning Robot Platform using Game Engine. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2020(0). 2A1–D04. 1 indexed citations
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Ito, Hiroshi, et al.. (2018). Development of Integration Method of Element Motions using Deep Learning. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2018(0). 1A1–D09. 1 indexed citations
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Ogata, Tetsuya, et al.. (2018). Adaptive Drawing Behavior by Visuomotor Learning Using Recurrent Neural Networks. IEEE Transactions on Cognitive and Developmental Systems. 11(1). 119–128. 4 indexed citations
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Suzuki, Kanata, et al.. (2017). Put-In-Box task generated from multiple discrete tasks by humanoid robot using deep learning. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2017(0). 1P2–N07. 6 indexed citations
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Noda, Kuniaki, et al.. (2016). Visual motor integration of robot’s drawing behavior using recurrent neural network. Robotics and Autonomous Systems. 86. 184–195. 22 indexed citations
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Goto, Masataka, et al.. (2010). Query-by-Example Music Information Retrieval by Score-Informed Source Separation and Remixing Technologies. SHILAP Revista de lepidopterología. 1 indexed citations
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Ogata, Tetsuya, Hiroshi G. Okuno, & Hideki Kozima. (2008). Motion from sound: Intermodal neural network mapping. IEEE Intelligent Systems. 23(2). 76–78. 2 indexed citations
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Ogata, Tetsuya, et al.. (2006). Detection and Range Finder of Intercepted Object by AH-based Method using Audible Sound. IEICE Technical Report; IEICE Tech. Rep.. 106(267). 1–6. 1 indexed citations
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Ogata, Tetsuya, et al.. (2000). Robotic co-operation system based on a self-organization approached human work model. International Conference on Robotics and Automation. 4. 4057–4062. 1 indexed citations
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Abe, Yoshiyuki, Yoshihisa Ohnishi, M Yoshimura, et al.. (1996). P-glycoprotein-mediated acquired multidrug resistance of human lung cancer cells in vivo. British Journal of Cancer. 74(12). 1929–1934. 26 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.

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