1 citations · 1 across the 4 of their papers we have counts for
4 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…
Rethinking Image Super-Resolution from Training Data Perspectives
Go Ohtani, Ryu Tadokoro, Ryosuke Yamada +7
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 com…