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October 16, 2025Open Access

ECMF: Enhanced Cross-Modal Fusion for Multimodal Emotion Recognition in MER-SEMI Challenge

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The approach significantly improves performance on the MER2025-SEMI dataset, validating its framework.
A dual-branch visual encoder captures varied features from images while enriching textual input through large language models.
Self-attention mechanisms dynamically weight multimodal features, enhancing integration for better recognition accuracy.
The method includes a multi-source labeling strategy to refine training data, which mitigates issues with noisy labels.

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Proposes a multimodal emotion recognition framework, achieving a weighted F-score of 87.49% in MER-SEMI competition.

Hu et al. (2025) studied this question.

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