A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion
This work addresses multimodal emotion recognition for AI and computer vision applications, but appears incremental as it builds on existing methods with hybrid techniques.
The paper tackled the problem of multimodal emotion recognition by addressing challenges in heterogeneous data fusion and modality correlations, proposing DeepMSI-MER, which improved accuracy and robustness on IEMOCAP and MELD datasets.
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.