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

arXiv: https://arxiv.org/abs/2502.08573 · HTML: https://mlsanity.com/paper/2502.08573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [Signal: Selective Interaction and Global-local Alignment for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2511.17965/) (2025)
- [Enhancing Multimodal Emotion Recognition via Multi-Feature Encoding and Attention-Based Fusion](https://mlsanity.com/paper/2609.04690/) (2026)

## Abstract (arXiv)

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.