CCAF: Coarse-to-fine Cross-Modal Alignment and Fusion for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) has witnessed remarkable advancements in recent years. Existing MSA methods focus primarily on learning coarse-grained representations from different modalities to perform global cross-modal alignment or fusion. However, these approaches often neglect fine-grained valuable sentimental clues derived from local cross-modal interactions. Furthermore, the cross-modal alignment and fusion of complex global and local cross-modal information pose significant challenges in MSA tasks. To address this issue, we propose a novel MSA framework that simultaneously captures coarse-grained and fine-grained cross-modal sentiment cues through global and local cross-modal alignment and fusion. Our approach consists of three key components: i) optimal transport-based global and local cross-modal alignment, which separately aligns valuable global and local sentiment clues across modalities, ii) global and local cross-modal gated attention, which respectively fuse the aligned global and local cross-modal representations, and iii) prototype-informed information bottleneck, which utilizes learnable sentiment prototypes and contrastive prototype match to eliminate redundant cross-modal information at both global and local levels. Extensive experiments conducted on two publicly available MSA datasets demonstrate the effectiveness and superiority of our proposed model.