MCUR improves multimodal emotion recognition across heterogeneous modality setups by combining modality-combination contrastive learning with sample-wise uncertainty regularization, yielding F1 gains of 2.2-4.37% on MOSI, MOSEI, and IEMOCAP.
Iemocap: Interactive emotional dyadic motion capture database
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An LLM-assisted annotation pipeline creates the PodSarc sarcastic speech dataset from podcasts and validates it via a collaborative gating detection model reaching 73.63% F1.
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Modality-Aware Contrastive and Uncertainty-Regularized Emotion Recognition
MCUR improves multimodal emotion recognition across heterogeneous modality setups by combining modality-combination contrastive learning with sample-wise uncertainty regularization, yielding F1 gains of 2.2-4.37% on MOSI, MOSEI, and IEMOCAP.
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Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection
An LLM-assisted annotation pipeline creates the PodSarc sarcastic speech dataset from podcasts and validates it via a collaborative gating detection model reaching 73.63% F1.