🤖 AI Summary
RGB-D multimodal fusion mechanisms have long suffered from poor interpretability, and the fundamental nature of cross-modal complementarity remains unclear. Method: This paper establishes the first interpretability analysis framework specifically targeting the fusion process, introducing a joint metric of semantic variance and feature similarity to systematically characterize cross-modal representation consistency, intra-modal evolutionary patterns, and collaborative optimization logic. Through cross-layer feature comparison and quantitative semantic analysis, we identify a prevalent imbalance between consistency and specificity in mainstream fusion strategies. Contribution/Results: We formalize a “specificity-driven inference under consistency constraints” principle that explicates cross-modal complementarity. Our framework provides both theoretical foundations and a verifiable evaluation paradigm for designing trustworthy, generalizable multimodal fusion models.
📝 Abstract
In this paper, we present an analytical framework and a novel metric to shed light on the interpretation of the multimodal vision community. Our approach involves measuring the proposed semantic variance and feature similarity across modalities and levels, and conducting semantic and quantitative analyses through comprehensive experiments. Specifically, we investigate the consistency and speciality of representations across modalities, evolution rules within each modality, and the collaboration logic used when optimizing a multi-modality model. Our studies reveal several important findings, such as the discrepancy in cross-modal features and the hybrid multi-modal cooperation rule, which highlights consistency and speciality simultaneously for complementary inference. Through our dissection and findings on multi-modal fusion, we facilitate a rethinking of the reasonability and necessity of popular multi-modal vision fusion strategies. Furthermore, our work lays the foundation for designing a trustworthy and universal multi-modal fusion model for a variety of tasks in the future.
