Progressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalities

Multimodal Sentiment Analysis (MSA) seeks to infer humanemotions by integrating textual, acoustic, and visual cues. However, ex-isting approaches often rely on all modalities are completeness, whereasreal-world applications frequently encounter noise, hardware failures, orprivacy restrictions that result in missing modalities. There exists a sig-nificant feature misalignment between incomplete and complete modali-ties, and directly fusing them may even distort the well-learned represen-tations of the intact modalities. To this end, we propose PRLF, a Pro-gressive Representation Learning Framework designed for MSA underuncertain missing-modality conditions. PRLF introduces an AdaptiveModality Reliability Estimator (AMRE), which dynamically quantifiesthe reliability of each modality using recognition confidence and Fisherinformation to determine the dominant modality. In addition, the Pro-gressive Interaction (ProgInteract) module iteratively aligns the othermodalities with the dominant one, thereby enhancing cross-modal con-sistency while suppressing noise. Extensive experiments on CMU-MOSI,CMU-MOSEI, and SIMS verify that PRLF outperforms state-of-the-artmethods across both inter- and intra-modality missing scenarios, demon-strating its robustness and generalization capability.