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

What is it about?

Emotion recognition plays a vital role in enhancing human-computer interaction. In this study, we tackle the MER-SEMI challenge of the MER2025 competition by proposing a novel multimodal emotion recognition framework. To address the issue of data scarcity, we leverage large-scale pre-trained models to extract informative features from visual, audio, and textual modalities. Specifically, for the visual modality, we design a dual-branch visual encoder that captures both global frame-level features and localized facial representations. For the textual modality, we introduce a context-enriched method that employs large language models to enrich emotional cues within the input text. To effectively integrate these multimodal features, we propose a fusion strategy comprising two key components, i.e., self-attention mechanisms for dynamic modality weighting, and residual connections to preserve original representations. Beyond architectural design, we further refine noisy labels in the training set by a multi-source labeling strategy. Our approach achieves a substantial performance improvement over the official baseline on the MER2025-SEMI dataset, attaining a weighted F-score of 87.49 % compared to 78.63 %, thereby validating the effectiveness of the proposed framework.

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Photo by Alexas_Fotos on Unsplash

Photo by Alexas_Fotos on Unsplash

Why is it important?

This study proposes a multimodal emotion recognition framework to address the MER2025 challenge. Our contributions are threefold. • To address the issue of data scarcity, we leverage appropriate pre-trained models as multimodal feature extractors. Specifically, for visual modality, we design a dual-branch visual encoder that captures both global frame-level features and localized facial representations. For textual modality, we propose a context-enriched method using LLMs to enrich emotional cues in the text inputs. • To handle modality competition, we design a fusion strategy that dynamically weights different modalities to ensure robust performance. • Extensive experiments conducted on the official dataset demonstrate a significant improvement over the baseline, achieving a weighted F-score of 87.49 % compared to 78.63 %.

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