Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications

TL;DR

The paper addresses the challenge of robust, cross-domain, cross-modal image retrieval by synthesizing data augmentation, adversarial learning, and physics-informed optimization. It proposes a framework that uses cross-modal data augmentation to widen modality coverage, adversarial training to improve robustness, and physics-guided perturbations to ensure physical plausibility and transferability across domains. Key contributions include a taxonomy of augmentation strategies, a physics-informed adversarial framework, and an integrated pipeline for robust multi-modal retrieval. The work aims to advance secure, interpretable, and adaptable cross-modal systems applicable to vision tasks and scientific computing contexts, with implications for improved generalization under real-world conditions and adversarial threats.

Abstract

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 scientific computing. This review focuses on systematically analyzing how these two approaches can be synergistically integrated to enhance performance and robustness across diverse application domains. By addressing key obstacles such as modality discrepancies, limited data availability, and insufficient model robustness, this paper highlights the role of physics-based optimization frameworks in facilitating efficient and interpretable adversarial perturbation generation. The review also explores significant advancements in cross-modal adversarial learning, including applications in tasks such as image cross-modal retrieval (e.g., infrared and RGB matching), scientific computing (e.g., solving partial differential equations), and optimization under physical consistency constraints in vision systems. By examining theoretical foundations and experimental outcomes, this study demonstrates the potential of combining these approaches to handle complex scenarios and improve the security of multi-modal systems. Finally, we outline future directions, proposing a novel framework that unifies physical principles with adversarial optimization, providing a pathway for researchers to develop robust and adaptable cross-modal learning methods with both theoretical and practical significance.