5 papers
Learning to Fuse: Modality-Aware Adaptive Scheduling for Robust Multimodal Foundation Models
Liam Bennett, Mason Clark, Lucas Anderson +2
Multimodal foundation models have achieved impressive progress across a wide range of vision-language tasks. However, existing approaches often adopt fixed or task-specific fusion…
Dynamic Modality Scheduling for Multimodal Large Models via Confidence, Uncertainty, and Semantic Consistency
Hiroshi Tanaka, Anika Rao, Hana Satou +2
Multimodal Large Models (MLLMs) have achieved remarkable progress in vision-language understanding and generation tasks. However, existing MLLMs typically rely on static modality f…
GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation
Hana Satou, F Monkey
Domain adaptation remains a challenge when there is significant manifold discrepancy between source and target domains. Although recent methods leverage manifold-aware adversarial…
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning
Hana Satou, Alan Mitkiy
Transfer learning across domains with distribution shift remains a fundamental challenge in building robust and adaptable machine learning systems. While adversarial perturbations…
Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications
Hana Satou, Alan Mitkiy
The convergence of cross-modal adversarial learning and physics-driven methods represents a cutting-edge direction for tackling challenges in complex multi-modal tasks and scientif…