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arXiv · 2502.08573

A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion

Wei Dai·Dequan Zheng·Feng Yu·Yanrong Zhang·Yaohui Hou
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Abstract

With the advancement of artificial intelligence and computer vision technologies, multimodal emotion recognition has become a prominent research topic. However, existing methods face challenges such as heterogeneous data fusion and the effective utilization of modality correlations. This paper proposes a novel multimodal emotion recognition approach, DeepMSI-MER, based on the integration of contrastive learning and visual sequence compression. The proposed method enhances cross-modal feature fusion through contrastive learning and reduces redundancy in the visual modality by leveraging visual sequence compression. Experimental results on two public datasets, IEMOCAP and MELD, demonstrate that DeepMSI-MER significantly improves the accuracy and robustness of emotion recognition, validating the effectiveness of multimodal feature fusion and the proposed approach.

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Wei Dai, Dequan Zheng, Feng Yu, Yanrong Zhang, Yaohui Hou. 2025-02-12. A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion. https://arxiv.org/abs/2502.08573

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