5 papers
Object-Centric World Models for Causality-Aware Reinforcement Learning
Yosuke Nishimoto, Takashi Matsubara
World models have been developed to support sample-efficient deep reinforcement learning agents. However, it remains challenging for world models to accurately replicate environmen…
PHyCLIP: -Product of Hyperbolic Factors Unifies Hierarchy and Compositionality in Vision-Language Representation Learning
Daiki Yoshikawa, Takashi Matsubara
Vision-language models have achieved remarkable success in multi-modal representation learning from large-scale pairs of visual scenes and linguistic descriptions. However, they st…
Be Tangential to Manifold: Discovering Riemannian Metric for Diffusion Models
Shinnosuke Saito, Takashi Matsubara
Diffusion models are powerful deep generative models, but unlike classical models, they lack an explicit low-dimensional latent space that parameterizes the data manifold. This abs…
Image Interpolation with Score-based Riemannian Metrics of Diffusion Models
Shinnosuke Saito, Takashi Matsubara
Diffusion models excel in content generation by implicitly learning the data manifold, yet they lack a practical method to leverage this manifold - unlike other deep generative mod…
Predicated Diffusion: Predicate Logic-Based Attention Guidance for Text-to-Image Diffusion Models
Kota Sueyoshi, Takashi Matsubara
Diffusion models have achieved remarkable results in generating high-quality, diverse, and creative images. However, when it comes to text-based image generation, they often fail t…