Zobrazeno 1 - 10
of 354
pro vyhledávání: '"Yamada, Ryosuke"'
Autor:
Yamada, Ryosuke, Hara, Kensho, Kataoka, Hirokatsu, Makihara, Koshi, Inoue, Nakamasa, Yokota, Rio, Satoh, Yutaka
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 recognition have tended to process these modalities separately. We aim to
Externí odkaz:
http://arxiv.org/abs/2409.13535
Autor:
Ohtani, Go, Tadokoro, Ryu, Yamada, Ryosuke, Asano, Yuki M., Laina, Iro, Rupprecht, Christian, Inoue, Nakamasa, Yokota, Rio, Kataoka, Hirokatsu, Aoki, Yoshimitsu
In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate
Externí odkaz:
http://arxiv.org/abs/2409.00768
Autor:
Nakamura, Ryo, Tadokoro, Ryu, Yamada, Ryosuke, Asano, Yuki M., Laina, Iro, Rupprecht, Christian, Inoue, Nakamasa, Yokota, Rio, Kataoka, Hirokatsu
Pre-training and transfer learning are an important building block of current computer vision systems. While pre-training is usually performed on large real-world image datasets, in this paper we ask whether this is truly necessary. To this end, we s
Externí odkaz:
http://arxiv.org/abs/2408.00677
Publikováno v:
Proceedings of the British Machine Vision Conference (BMVC), 2023
The construction of 3D medical image datasets presents several issues, including requiring significant financial costs in data collection and specialized expertise for annotation, as well as strict privacy concerns for patient confidentiality compare
Externí odkaz:
http://arxiv.org/abs/2401.03665
Autor:
Kataoka, Hirokatsu, Hayamizu, Ryo, Yamada, Ryosuke, Nakashima, Kodai, Takashima, Sora, Zhang, Xinyu, Martinez-Noriega, Edgar Josafat, Inoue, Nakamasa, Yokota, Rio
In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of Vision Transformers (
Externí odkaz:
http://arxiv.org/abs/2206.09132
Autor:
Tamayama, Yasuhiro, Yamada, Ryosuke
Publikováno v:
J. Phys. D: Appl. Phys. 54 385103 (2021)
To realize efficient nonlinear metamaterials, we investigate a method for enhancing the local electric field in a metamolecule composed of two radiatively coupled cut-wire resonators where resonance of the cut-wire resonators and low-group-velocity p
Externí odkaz:
http://arxiv.org/abs/2108.04684
Autor:
Kataoka, Hirokatsu, Okayasu, Kazushige, Matsumoto, Asato, Yamagata, Eisuke, Yamada, Ryosuke, Inoue, Nakamasa, Nakamura, Akio, Satoh, Yutaka
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 Supervised Learning. We automatically generate image patterns and the
Externí odkaz:
http://arxiv.org/abs/2101.08515
Autor:
Okamura, Taiki, Aritomi, Rina, Matsumoto, Takuya, Yamada, Ryosuke, Hirakawa, Hidehiko, Ogino, Hiroyasu
Publikováno v:
In Biochemical Engineering Journal January 2024 201
Publikováno v:
In Enzyme and Microbial Technology August 2023 168
Publikováno v:
In Biochemical Engineering Journal February 2023 191