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.

通过提供一个分析框架和新的度量标准，我们研究了多模态视觉社区的解释。通过实验，我们调查了不同模态之间的一致性和特殊性，模态内的演化规则，以及优化多模态模型时使用的协作逻辑，并揭示了一些重要发现，这些发现有助于重新思考流行的多模态视觉融合策略的合理性和必要性，从而为未来设计一个可信赖和通用的多模态融合模型奠定了基础。