7 citations · 14 across the 16 of their papers we have counts for
6 papers · 1 filter
FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss
Hina Otake, Shinei Arakawa, Keitaro Tanaka +3
Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing methods cast pre-training as classification on discretized labels, discardin…
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…
Beyond Single Object: Learning 3D Relations with Large Language Models
Kohsuke Ide, Ryousuke Yamada, Yue Qiu +4
We address a fundamental gap in 3D-LLMs: existing models focus on single-object/scene description, struggling with detailed, inter-object comparison. We propose a framework for det…
Seeing Red, Thinking Bad: Color Bias in Vision Language Models
Kohsuke Ide, Ryousuke Yamada, Yoshihiro Fukuhara +2
Vision language models (VLMs) are increasingly used in industrial decision-making systems, such as recruitment support and recommendation. This motivates careful analysis of how VL…
Feed-forward Motion In-betweening for Any 4D
Hiroki Nishizawa, Hubert P. H. Shum, Yoshihiro Fukuhara +2
4D dynamics (3D geometry evolving over time) is a fundamental representation of the physical world and plays a crucial role in world modeling (e.g., animation and games). Owing to…
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…