no code implementations • 8 Aug 2023 • Tomoki Arai, Kenji Iwata, Kensho Hara, Yutaka Satoh
Drones are being used to assess the situation in various disasters.
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.
2 code implementations • 21 Jan 2021 • Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh
Is it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding?
10 code implementations • 10 Apr 2020 • Hirokatsu Kataoka, Tenga Wakamiya, Kensho Hara, Yutaka Satoh
Therefore, in the present paper, we conduct exploration study in order to improve spatiotemporal 3D CNNs as follows: (i) Recently proposed large-scale video datasets help improve spatiotemporal 3D CNNs in terms of video classification accuracy.
1 code implementation • 27 Mar 2020 • Munetaka Minoguchi, Ken Okayama, Yutaka Satoh, Hirokatsu Kataoka
To construct an algorithm that can provide robust person detection, we present a dataset with over 8 million images that was produced in a weakly supervised manner.
no code implementations • CVPR 2018 • Tomoyuki Suzuki, Hirokatsu Kataoka, Yoshimitsu Aoki, Yutaka Satoh
In this paper, we propose a novel approach for traffic accident anticipation through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a large-scale self-annotated incident database for anticipation.
no code implementations • 7 Apr 2018 • Hirokatsu Kataoka, Teppei Suzuki, Shoko Oikawa, Yasuhiro Matsui, Yutaka Satoh
Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely.
26 code implementations • CVPR 2018 • Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh
The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels.
Ranked #49 on Action Recognition on UCF101
1 code implementation • 25 Aug 2017 • Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh
The 3D ResNets trained on the Kinetics did not suffer from overfitting despite the large number of parameters of the model, and achieved better performance than relatively shallow networks, such as C3D.
no code implementations • CVPR 2017 • Ryusuke Sagawa, Yutaka Satoh
In this paper, we propose a method of energy-efficient active illumination in an environment with severe external lights.
no code implementations • 10 May 2017 • Hirokatsu Kataoka, Kaori Abe, Akio Nakamura, Yutaka Satoh
The paper presents a novel concept for collaborative descriptors between deeply learned and hand-crafted features.
3 code implementations • 23 Mar 2017 • Kaori Abe, Teppei Suzuki, Shunya Ueta, Akio Nakamura, Yutaka Satoh, Hirokatsu Kataoka
The paper presents a novel concept that analyzes and visualizes worldwide fashion trends.
no code implementations • 30 Aug 2016 • Hirokatsu Kataoka, Yun He, Soma Shirakabe, Yutaka Satoh
Information of time differentiation is extremely important cue for a motion representation.
no code implementations • 29 Aug 2016 • Yun He, Soma Shirakabe, Yutaka Satoh, Hirokatsu Kataoka
The objective of this paper is to evaluate "human action recognition without human".
no code implementations • 1 May 2016 • Hirokatsu Kataoka, Masaki Hayashi, Kenji Iwata, Yutaka Satoh, Yoshimitsu Aoki, Slobodan Ilic
Latent Dirichlet allocation (LDA) is used to develop approximations of human motion primitives; these are mid-level representations, and they adaptively integrate dominant vectors when classifying human activities.
no code implementations • 26 Apr 2016 • Teppei Suzuki, Soma Shirakabe, Yudai Miyashita, Akio Nakamura, Yutaka Satoh, Hirokatsu Kataoka
By the detected change areas, however, a human cannot understand how different the two images.
no code implementations • 25 Sep 2015 • Hirokatsu Kataoka, Kenji Iwata, Yutaka Satoh
In this paper, we evaluate convolutional neural network (CNN) features using the AlexNet architecture and very deep convolutional network (VGGNet) architecture.