Search Results for author: Shintaro Yamamoto

Found 5 papers, 1 papers with code

Community-Driven Comprehensive Scientific Paper Summarization: Insight from cvpaper.challenge

no code implementations17 Mar 2022 Shintaro Yamamoto, Hirokatsu Kataoka, Ryota Suzuki, Seitaro Shinagawa, Shigeo Morishima

To alleviate this problem, we organized a group of non-native English speakers to write summaries of papers presented at a computer vision conference to share the knowledge of the papers read by the group.

Describing and Localizing Multiple Changes with Transformers

2 code implementations ICCV 2021 Yue Qiu, Shintaro Yamamoto, Kodai Nakashima, Ryota Suzuki, Kenji Iwata, Hirokatsu Kataoka, Yutaka Satoh

Change captioning tasks aim to detect changes in image pairs observed before and after a scene change and generate a natural language description of the changes.

Self-Supervised Learning for Visual Summary Identification in Scientific Publications

no code implementations21 Dec 2020 Shintaro Yamamoto, Anne Lauscher, Simone Paolo Ponzetto, Goran Glavaš, Shigeo Morishima

Providing visual summaries of scientific publications can increase information access for readers and thereby help deal with the exponential growth in the number of scientific publications.

Self-Supervised Learning

Automatic Paper Summary Generation from Visual and Textual Information

no code implementations16 Nov 2018 Shintaro Yamamoto, Yoshihiro Fukuhara, Ryota Suzuki, Shigeo Morishima, Hirokatsu Kataoka

Due to the recent boom in artificial intelligence (AI) research, including computer vision (CV), it has become impossible for researchers in these fields to keep up with the exponentially increasing number of manuscripts.

Understanding Fake Faces

no code implementations22 Sep 2018 Ryota Natsume, Kazuki Inoue, Yoshihiro Fukuhara, Shintaro Yamamoto, Shigeo Morishima, Hirokatsu Kataoka

Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms.

Face Recognition Face Verification

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