114 citations · 334 across the 16 of their papers we have counts for
16 papers
Estimation of Human Condition at Disaster Site Using Aerial Drone Images
Tomoki Arai, Kenji Iwata, Kensho Hara +1
Drones are being used to assess the situation in various disasters. In this study, we investigate a method to automatically estimate the damage status of people based on their acti…
Describing and Localizing Multiple Changes with Transformers
Yue Qiu, Shintaro Yamamoto, Kodai Nakashima +4
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. Existing change ca…
Pre-training without Natural Images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto +5
Is it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding? The paper proposes a novel concept, Formula-driven…
Weakly Supervised Dataset Collection for Robust Person Detection
Munetaka Minoguchi, Ken Okayama, Yutaka Satoh +1
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. Through labor-i…
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
Hirokatsu Kataoka, Tenga Wakamiya, Kensho Hara +1
How can we collect and use a video dataset to further improve spatiotemporal 3D Convolutional Neural Networks (3D CNNs)? In order to positively answer this open question in video r…
Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB
Tomoyuki Suzuki, Hirokatsu Kataoka, Yoshimitsu Aoki +1
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 inciden…
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Hirokatsu Kataoka, Teppei Suzuki, Shoko Oikawa +2
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 (…
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
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…
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh
Convolutional neural networks with spatio-temporal 3D kernels (3D CNNs) have an ability to directly extract spatio-temporal features from videos for action recognition. Although th…
Collaborative Descriptors: Convolutional Maps for Preprocessing
Hirokatsu Kataoka, Kaori Abe, Akio Nakamura +1
The paper presents a novel concept for collaborative descriptors between deeply learned and hand-crafted features. To achieve this concept, we apply convolutional maps for pre-proc…
Changing Fashion Cultures
Kaori Abe, Teppei Suzuki, Shunya Ueta +3
The paper presents a novel concept that analyzes and visualizes worldwide fashion trends. Our goal is to reveal cutting-edge fashion trends without displaying an ordinary fashion s…
Motion Representation with Acceleration Images
Hirokatsu Kataoka, Yun He, Soma Shirakabe +1
Information of time differentiation is extremely important cue for a motion representation. We have applied first-order differential velocity from a positional information, moreove…
Human Action Recognition without Human
Hirokatsu Kataoka, Kensho Hara, Yutaka Satoh
The objective of this paper is to evaluate "human action recognition without human". Motion representation is frequently discussed in human action recognition. We have examined sev…
Dominant Codewords Selection with Topic Model for Action Recognition
Hirokatsu Kataoka, Masaki Hayashi, Kenji Iwata +3
In this paper, we propose a framework for recognizing human activities that uses only in-topic dominant codewords and a mixture of intertopic vectors. Latent Dirichlet allocation (…
Semantic Change Detection with Hypermaps
Teppei Suzuki, Soma Shirakabe, Yudai Miyashita +3
Change detection is the study of detecting changes between two different images of a scene taken at different times. By the detected change areas, however, a human cannot understan…
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection
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. To date, most CNN r…