Text-Anchored Modality Experts with Uncertainty-Guided Routing for Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) aims to understand user-generated videos by integrating multiple modalities. Existing methods often treat modalities as symmetric, overlooking the fact that language provides a dominant semantic prior while nonverbal signals are complementary and noise-prone. To address this, we propose a Text-Anchored Modality Experts with Uncertainty-Guided Routing, termed TAME, that reformulates MSA as an uncertainty-guided refinement paradigm, where the language prior is calibrated and enhanced by selectively trusted nonverbal evidence. Specifically, TAME adopts an asymmetric Mixture-of-Modality-Experts framework, in which textual features act as a fixed and always-...
| Authors | Xiaohua Huang, Bingrui Lei, Xiuyan hao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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