114 citations · 277 across the 10 of their papers we have counts for
10 papers
Can masking background and object reduce static bias for zero-shot action recognition?
Takumi Fukuzawa, Kensho Hara, Hirokatsu Kataoka +1
In this paper, we address the issue of static bias in zero-shot action recognition. Action recognition models need to represent the action itself, not the appearance. However, some…
Text-guided Synthetic Geometric Augmentation for Zero-shot 3D Understanding
Kohei Torimi, Ryosuke Yamada, Daichi Otsuka +4
Zero-shot recognition models require extensive training data for generalization. However, in zero-shot 3D classification, collecting 3D data and captions is costly and laborintensi…
Formula-Supervised Visual-Geometric Pre-training
Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka +4
Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object rec…
Traffic Incident Database with Multiple Labels Including Various Perspective Environmental Information
Shota Nishiyama, Takuma Saito, Ryo Nakamura +3
A large dataset of annotated traffic accidents is necessary to improve the accuracy of traffic accident recognition using deep learning models. Conventional traffic accident datase…
Diffusion-based Holistic Texture Rectification and Synthesis
Guoqing Hao, Satoshi Iizuka, Kensho Hara +3
We present a novel framework for rectifying occlusions and distortions in degraded texture samples from natural images. Traditional texture synthesis approaches focus on generating…
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…
Retrieving and Highlighting Action with Spatiotemporal Reference
Seito Kasai, Yuchi Ishikawa, Masaki Hayashi +3
In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work tak…
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…
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…