Text-Anchored Modality Experts with Uncertainty-Guided Routing for Multimodal Sentiment Analysis

Xiaohua Huang; Bingrui Lei; Xiuyan Shao; Ling Zhou; Yuan Zong; Wenming Zheng; Guoying Zhao

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arXiv · 2610.27571

Text-Anchored Modality Experts with Uncertainty-Guided Routing for Multimodal Sentiment Analysis

Xiaohua Huang·Bingrui Lei·Xiuyan Shao·Ling Zhou·Yuan Zong·Wenming Zheng·Guoying Zhao

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Abstract

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-activated shared expert, while audio-textual and video-textual cross-modal branches serve as dynamically routed auxiliary experts. Furthermore, we introduce lightweight Prophet Heads to quantify the sample-level predictive uncertainty of each individual modality, and this uncertainty metric is further incorporated into the modality router to realize reliability-aware expert aggregation and fusion. Experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate that TAME achieves state-of-the-art performance on the majority of metrics while reducing parameter count by up to 78.7% and FLOPs by up to 41.2% over the mixture-of-expert baseline.

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BibTeX RIS

Xiaohua Huang, Bingrui Lei, Xiuyan Shao, Ling Zhou, Yuan Zong, Wenming Zheng, Guoying Zhao. 2026-10-21. Text-Anchored Modality Experts with Uncertainty-Guided Routing for Multimodal Sentiment Analysis. https://arxiv.org/abs/2610.27571

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