Kensho Hara

3.3k total citations · 2 hit papers
20 papers, 1.8k citations indexed

About

Kensho Hara is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Kensho Hara has authored 20 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 4 papers in Biomedical Engineering. Recurrent topics in Kensho Hara's work include Human Pose and Action Recognition (11 papers), Video Surveillance and Tracking Methods (6 papers) and Anomaly Detection Techniques and Applications (6 papers). Kensho Hara is often cited by papers focused on Human Pose and Action Recognition (11 papers), Video Surveillance and Tracking Methods (6 papers) and Anomaly Detection Techniques and Applications (6 papers). Kensho Hara collaborates with scholars based in Japan. Kensho Hara's co-authors include Hirokatsu Kataoka, Yutaka Satoh, Ryozo Noguchi, Kenji Mase, Takatsugu Hirayama, Kenichi Narioka, Xueting Wang, Ryohei Nakano, Nakamasa Inoue and Tetsuya Nakazaki and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Intelligent Transportation Systems and IEICE Transactions on Information and Systems.

In The Last Decade

Kensho Hara

19 papers receiving 1.8k citations

Hit Papers

Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs... 2017 2026 2020 2023 2018 2017 400 800 1.2k
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. Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
    2018 1.4k citations Kensho Hara, Hirokatsu Kataoka et al. profile →
  2. Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
    2017 413 citations Kensho Hara, Hirokatsu Kataoka et al. profile →

Peers

Kensho Hara
Hirokatsu Kataoka Japan
Yutaka Satoh Japan
Jamie Ray United States
Hakan Bilen United Kingdom
Joe Yue-Hei Ng United States
Zhaofan Qiu China
Dima Damen United Kingdom
Sudheendra Vijayanarasimhan United States
Basura Fernando Australia
Efstratios Gavves Netherlands
Hirokatsu Kataoka Japan
Kensho Hara
Citations per year, relative to Kensho Hara Kensho Hara (= 1×) peers Hirokatsu Kataoka

Countries citing papers authored by Kensho Hara

Since Specialization
Citations

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

Fields of papers citing papers by Kensho Hara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kensho Hara

This figure shows the co-authorship network connecting the top 25 collaborators of Kensho Hara. A scholar is included among the top collaborators of Kensho Hara 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 Kensho Hara. Kensho Hara 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
1.
Nakamura, Ryô, et al.. (2024). Pseudo-Outlier Synthesis Using Q-Gaussian Distributions for Out-of-Distribution Detection. 3120–3124. 1 indexed citations
2.
Kataoka, Hirokatsu, et al.. (2023). Transformer-based ripeness segmentation for tomatoes. SHILAP Revista de lepidopterología. 4. 100196–100196. 13 indexed citations
3.
Iizuka, Satoshi, et al.. (2023). Diffusion-based Holistic Texture Rectification and Synthesis. 1–11. 1 indexed citations
4.
Qiu, Yue, et al.. (2023). VirtualHome Action Genome: A Simulated Spatio-Temporal Scene Graph Dataset with Consistent Relationship Labels. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3340–3349. 1 indexed citations
5.
Hara, Kensho, et al.. (2023). RoseTracker: A system for automated rose growth monitoring. SHILAP Revista de lepidopterología. 5. 100271–100271. 10 indexed citations
6.
Iwata, Kenji, et al.. (2023). Estimation of Human Condition at Disaster Site Using Aerial Drone Images. 3777–3785. 2 indexed citations
7.
Kataoka, Hirokatsu, et al.. (2022). Spatiotemporal Initialization for 3D CNNs with Generated Motion Patterns. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 737–746. 2 indexed citations
8.
Hara, Kensho, et al.. (2021). Predicting Appearance of Vehicles From Blind Spots Based on Pedestrian Behaviors at Crossroads. IEEE Transactions on Intelligent Transportation Systems. 23(8). 11917–11929. 4 indexed citations
9.
Hara, Kensho, et al.. (2021). Rethinking Training Data for Mitigating Representation Biases in Action Recognition. 3344–3348. 8 indexed citations
10.
Hara, Kensho, et al.. (2020). Predicting Vehicles Appearing from Blind Spots Based on Pedestrian Behaviors. 1–8. 6 indexed citations
11.
Hara, Kensho. (2020). Recent Advances in Video Action Recognition with 3D Convolutions. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. E104.A(6). 846–856. 4 indexed citations
12.
Hara, Kensho, Hirokatsu Kataoka, & Yutaka Satoh. (2018). Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?. 6546–6555. 1351 indexed citations breakdown →
13.
Hara, Kensho, Hirokatsu Kataoka, & Yutaka Satoh. (2018). Towards Good Practice for Action Recognition with Spatiotemporal 3D Convolutions. 2516–2521. 15 indexed citations
14.
Hara, Kensho, Hirokatsu Kataoka, & Yutaka Satoh. (2017). Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition. 3154–3160. 413 indexed citations breakdown →
15.
Wang, Xueting, et al.. (2017). Personal Viewpoint Navigation Based on Object Trajectory Distribution for Multi-View Videos. IEICE Transactions on Information and Systems. E101.D(1). 193–204. 1 indexed citations
16.
Wang, Xueting, et al.. (2017). User Group based Viewpoint Recommendation using User Attributes for Multiview Videos. 3–9. 5 indexed citations
17.
Hayashi, Chihiro, et al.. (2016). Sleeping Posture Classification Using E-Textile Pressure Sensor. IEICE Technical Report; IEICE Tech. Rep.. 115(414). 41–46. 1 indexed citations
18.
19.
Hara, Kensho, Takatsugu Hirayama, & Kenji Mase. (2014). Trend-sensitive hough forests for action detection. 1475–1479.
20.
Hara, Kensho, Takatsugu Hirayama, & Kenji Mase. (2013). Simultaneous Action Recognition and Localization Based on Multi-view Hough Voting. 616–620. 3 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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