no code implementations • CVPR 2018 • Atsushi Kanehira, Luc van Gool, Yoshitaka Ushiku, Tatsuya Harada
To satisfy these requirements (A)-(C) simultaneously, we proposed a novel video summarization method from multiple groups of videos.
no code implementations • CVPR 2016 • Katsunori Ohnishi, Atsushi Kanehira, Asako Kanezaki, Tatsuya Harada
We present a novel dataset and a novel algorithm for recognizing activities of daily living (ADL) from a first-person wearable camera.
no code implementations • 21 Feb 2015 • Ken Miura, Tetsuaki Mano, Atsushi Kanehira, Yuichiro Tsuchiya, Tatsuya Harada
Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript.
no code implementations • CVPR 2019 • Atsushi Kanehira, Tatsuya Harada
This paper addresses the generation of explanations with visual examples.
no code implementations • CVPR 2019 • Atsushi Kanehira, Kentaro Takemoto, Sho Inayoshi, Tatsuya Harada
This study addresses generating counterfactual explanations with multimodal information.
no code implementations • CVPR 2016 • Atsushi Kanehira, Tatsuya Harada
Such a setting has been studied as a positive and unlabeled (PU) classification problem in a binary setting.
no code implementations • 8 Jun 2021 • Tommi Kerola, Jie Li, Atsushi Kanehira, Yasunori Kudo, Alexis Vallet, Adrien Gaidon
We use a hierarchical Lov\'asz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals.
no code implementations • CVPR 2021 • Tommi Kerola, Jie Li, Atsushi Kanehira, Yasunori Kudo, Alexis Vallet, Adrien Gaidon
We use a hierarchical Lovasz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals.
no code implementations • 18 Oct 2023 • Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu, Katsushi Ikeuchi
This technical report explores the ability of ChatGPT in recognizing emotions from text, which can be the basis of various applications like interactive chatbots, data annotation, and mental health analysis.
no code implementations • 20 Nov 2023 • Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu, Katsushi Ikeuchi
The computation starts by analyzing the videos with GPT-4V to convert environmental and action details into text, followed by a GPT-4-empowered task planner.