Towards Robust Multimodal Sentiment Analysis Under Uncertain Signal Missing

Multimodal Sentiment Analysis (MSA) has attracted widespread research attention recently. Most MSA studies are based on the assumption of signal completeness. However, many inevitable factors in real applications lead to uncertain signal missing, causing significant degradation of model performance. To this end, we propose a
R
obust multimodal
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issing
S
ignal
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ramework (RMSF) to handle the problem of uncertain signal missing for MSA tasks and can be generalized to other multimodal patterns. Specifically, a hierarchical crossmodal interaction module in RMSF exploits potential complementary semantics among modalities via coarse- and fine-grained crossmodal attention. Furthermore, we design an adaptive feature refinement module to enhance the beneficial semantics of modalities and filter redundant features. Finally, we propose a knowledge-integrated self-distillation module that enables dynamic knowledge integration and bidirectional knowledge transfer within a single network to precisely reconstruct missing semantics. Comprehensive experiments are conducted on two datasets, indicating that RMSF significantly improves MSA performance under both uncertain missing-signal and complete-signal cases.