通过考虑模态噪声与迭代推理，提升对话中多模态情绪识别的鲁棒性。
SURE: Synergistic Uncertainty-aware Reasoning for Multimodal Emotion Recognition in Conversations
- 引入不确定性感知的专家混合模块，处理不同模态的噪声问题。
- 在多个基准数据集上优于当前最优方法，显著提升识别准确率。
- 适合需要高鲁棒性的对话情绪分析场景，如智能客服、心理辅助。
对话中的多模态情绪识别（MERC）需要融合多源信号，同时应对噪声并建模上下文推理。现有方法多关注特征融合，却忽视了噪声特征中的不确定性以及细粒度推理过程。本文提出SURE（Synergistic Uncertainty-aware REasoning）框架，通过三个组件增强鲁棒性与上下文建模能力：不确定性感知的专家混合模块用于处理模态特异性噪声；迭代推理模块实现对多轮对话的上下文推理；Transformer门控模块捕捉模态内与模态间交互。在多个基准MERC数据集上的实验表明，SURE持续优于现有最先进方法，验证了其在鲁棒多模态推理中的有效性。结果强调了不确定性建模与迭代推理在推进对话情绪识别中的关键作用。
原文摘要 · Abstract (English)
Multimodal emotion recognition in conversations (MERC) requires integrating multimodal signals while being robust to noise and modeling contextual reasoning. Existing approaches often emphasize fusion but overlook uncertainty in noisy features and fine-grained reasoning. We propose SURE (Synergistic Uncertainty-aware REasoning) for MERC, a framework that improves robustness and contextual modeling. SURE consists of three components: an Uncertainty-Aware Mixture-of-Experts module to handle modality-specific noise, an Iterative Reasoning module for multi-turn reasoning over context, and a Transformer Gate module to capture intra- and inter-modal interactions. Experiments on benchmark MERC datasets show that SURE consistently outperforms state-of-the-art methods, demonstrating its effectiveness in robust multimodal reasoning. These results highlight the importance of uncertainty modeling and iterative reasoning in advancing emotion recognition in conversational settings.
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