1 citations · 2 across the 5 of their papers we have counts for
5 papers
Primitive Geometry Segment Pre-training for 3D Medical Image Segmentation
Ryu Tadokoro, Ryosuke Yamada, Kodai Nakashima +2
The construction of 3D medical image datasets presents several issues, including requiring significant financial costs in data collection and specialized expertise for annotation,…
SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning
Risa Shinoda, Ryo Hayamizu, Kodai Nakashima +3
Pre-training is a strong strategy for enhancing visual models to efficiently train them with a limited number of labeled images. In semantic segmentation, creating annotation masks…
Pre-training Vision Transformers with Formula-driven Supervised Learning
Hirokatsu Kataoka, Sora Takashima, Ryo Hayamizu +6
In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k and can approach that of the JFT-300M d…
Can Vision Transformers Learn without Natural Images?
Kodai Nakashima, Hirokatsu Kataoka, Asato Matsumoto +2
Can we complete pre-training of Vision Transformers (ViT) without natural images and human-annotated labels? Although a pre-trained ViT seems to heavily rely on a large-scale datas…
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…