7 papers · 1 filter
Visual General Intelligence: A White Paper
Hirokatsu Kataoka, Yoshihiro Fukuhara, Yonglong Tian +18
This paper reconsiders intelligence from a vision-centered perspective and examines whether intelligence emerging from visual experience and learning may provide a pathway toward A…
Physics-IQ Verified
Tim Rädsch, Yuki M Asano, Hilde Kuehne +4
Video generative models ( VGMs) have become a new frontier that can be used not just for video generation but for a multitude of downstream tasks, including world modeling. To adva…
MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness
Yuto Matsuo, Yoshihiro Fukuhara, Yuki M. Asano +3
Data augmentation is a key technique for improving the robustness of image classification models. However, many recent approaches rely on diffusion-based synthesis or complex featu…
3D sans 3D Scans: Scalable Pre-training from Video-Generated Point Clouds
Ryousuke Yamada, Kohsuke Ide, Yoshihiro Fukuhara +4
Despite recent progress in 3D self-supervised learning, collecting large-scale 3D scene scans remains expensive and labor-intensive. In this work, we investigate whether 3D represe…
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…
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…
Scaling Backwards: Minimal Synthetic Pre-training?
Ryo Nakamura, Ryu Tadokoro, Ryosuke Yamada +6
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, i…