MCN-CL: Multimodal Cross-Attention Network and Contrastive Learning for Multimodal Emotion Recognition

TL;DR

This paper introduces MCN-CL, a novel multimodal emotion recognition framework that leverages cross-attention and contrastive learning to effectively fuse features and address challenges like modal heterogeneity and class imbalance, achieving superior performance on benchmark datasets.

Contribution

The paper proposes a new multimodal fusion framework using cross-attention and contrastive learning, improving emotion recognition accuracy in social media scenarios.

Findings

Outperforms state-of-the-art methods on IEMOCAP and MELD datasets.

Achieves 3.42% and 5.73% higher Weighted F1 scores respectively.

Effectively handles modal heterogeneity and category imbalance.

Abstract

Multimodal emotion recognition plays a key role in many domains, including mental health monitoring, educational interaction, and human-computer interaction. However, existing methods often face three major challenges: unbalanced category distribution, the complexity of dynamic facial action unit time modeling, and the difficulty of feature fusion due to modal heterogeneity. With the explosive growth of multimodal data in social media scenarios, the need for building an efficient cross-modal fusion framework for emotion recognition is becoming increasingly urgent. To this end, this paper proposes Multimodal Cross-Attention Network and Contrastive Learning (MCN-CL) for multimodal emotion recognition. It uses a triple query mechanism and hard negative mining strategy to remove feature redundancy while preserving important emotional cues, effectively addressing the issues of modal heterogeneity and category imbalance. Experiment results on the IEMOCAP and MELD datasets show that our proposed method outperforms state-of-the-art approaches, with Weighted F1 scores improving by 3.42% and 5.73%, respectively.