Sato Laboratory/Sugano Laboratory, UTokyo-IIS

Our group consists of two independent research laboratories (Y. Sato Lab. and Sugano Lab.) working closely together with a focus on computer vision.

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News

Jury Selections at 24th JAPAN MEDIA ARTS FESTIVAL

AICOM project was chosen as Jury Selections at 24th JAPAN MEDIA ARTS FESTIVAL, Entertainment Division.

Last updated on Mar 23, 2021

Jury Selections at 24th JAPAN MEDIA ARTS FESTIVAL

Featured Publications

Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation

Hand segmentation is a crucial task in first-person vision. Since first-person images exhibit strong bias in appearance among different …

Takehiko Ohkawa, Takuma Yagi, Atsushi Hashimoto, Yoshitaka Ushiku, Yoichi Sato

Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation

Spatio-Temporal Perturbations for Video Attribution

The attribution method provides a direction for interpreting opaque neural networks in a visual way by identifying and visualizing the …

Zhenqiang Li, Weimin Wang, Zuoyue Li, Yifei Huang, Yoichi Sato

Spatio-Temporal Perturbations for Video Attribution

GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery

People spend an enormous amount of time and effort looking for lost objects. To help remind people of the location of lost objects, …

Takuma Yagi, Takumi Nishiyasu, Kunimasa Kawasaki, Moe Matsuki, Yoichi Sato

GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery

Toward Visually Explaining Video Understanding Networks by Perturbation

‘‘Making black box models explainable’’ is a vital problem that accompanies the development of deep learning …

Zhenqiang Li, Weimin Wang, Zuoyue Li, Yifei Huang, Yoichi Sato

Toward Visually Explaining Video Understanding Networks by Perturbation

Community Detection Using Restrained Random-Walk Similarity

In this paper, we propose a restrained random-walk similarity method for detecting the community structures of graphs. The basic …

Makoto Okuda, Shin'ichi Sato, Yoichi Sato, Yutaka Kidawara

Community Detection Using Restrained Random-Walk Similarity

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