  The inherent challenge of multimodal fusion is to precisely capture the
cross-modal correlation and flexibly conduct cross-modal interaction. To fully
release the value of each modality and mitigate the influence of low-quality
multimodal data, dynamic multimodal fusion emerges as a promising learning
paradigm. Despite its widespread use, theoretical justifications in this field
are still notably lacking. Can we design a provably robust multimodal fusion
method? This paper provides theoretical understandings to answer this question
under a most popular multimodal fusion framework from the generalization
perspective. We proceed to reveal that several uncertainty estimation solutions
are naturally available to achieve robust multimodal fusion. Then a novel
multimodal fusion framework termed Quality-aware Multimodal Fusion (QMF) is
proposed, which can improve the performance in terms of classification accuracy
and model robustness. Extensive experimental results on multiple benchmarks can
support our findings.
