EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation
Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance. However, existing fusion approaches in MERC often ignore modality-specific uncertainty across utterances caused by conflicting cues, varying noise, and missing modality-specific signals. We propose EmoEUS, an explicit uncertainty supervision framework for MERC. EmoEUS performs uncertainty-aware multimodal fusion by dynamically weighting modalities using learned variance estimates. We also introduce an explicitly supervised loss that aligns each utterance's predicted variance with the distance between the utterance's distributional representation and its emoti...
| Authors | Zilong Huang, Kong Aik Lee, Junjie Li et al. |
|---|---|
| Primary category | cs.MM |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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