83 citations · 174 across the 6 of their papers we have counts for
10 papers
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 (…