AI RESEARCH
Improving Multimodal Sentiment Analysis via Modality Optimization and Dynamic Primary Modality Selection
arXiv CS.CV
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ArXi:2511.06328v3 Announce Type: replace Multimodal Sentiment Analysis (MSA) aims to predict sentiment from language, acoustic, and visual data in videos. However, imbalanced unimodal performance often leads to suboptimal fused representations. Existing approaches typically adopt fixed primary modality strategies to maximize dominant modality advantages, yet fail to adapt to dynamic variations in modality importance across different samples. Moreover, non-language modalities suffer from sequential redundancy and noise, degrading model performance when they serve as primary inputs.