Chun Ouyang

Explicitly modeling modality reliability can drastically enhance sentiment analysis performance in the face of incomplete multimodal data.

Multimodal sentiment analysis suffers from "branch imbalance," where shared representations become redundant and private representations lose discriminative power, but a new rebalancing framework can fix it.

Unlocking superior multimodal sentiment analysis, TSD reveals that disentangling features into common, pairwise, and private subspaces dramatically boosts performance.

By explicitly modeling a multi-level semantic hierarchy and carefully controlling information exchange between modalities, CLCR achieves state-of-the-art results in multimodal learning tasks ranging from emotion recognition to action recognition.