Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition

TL;DR

Joyful introduces a novel joint modality fusion and graph contrastive learning framework that enhances multimodal emotion recognition by effectively combining global and local features, achieving state-of-the-art results.

Contribution

The paper proposes a new multimodal fusion mechanism combined with graph contrastive learning to improve emotion recognition accuracy.

Findings

Achieved state-of-the-art performance on three benchmark datasets.

Effectively combines global contextual and uni-modal features.

Enhances discriminability of representations through contrastive learning.

Abstract

Multimodal emotion recognition aims to recognize emotions for each utterance of multiple modalities, which has received increasing attention for its application in human-machine interaction. Current graph-based methods fail to simultaneously depict global contextual features and local diverse uni-modal features in a dialogue. Furthermore, with the number of graph layers increasing, they easily fall into over-smoothing. In this paper, we propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition (Joyful), where multimodality fusion, contrastive learning, and emotion recognition are jointly optimized. Specifically, we first design a new multimodal fusion mechanism that can provide deep interaction and fusion between the global contextual and uni-modal specific features. Then, we introduce a graph contrastive learning framework with inter-view and intra-view contrastive losses to learn more distinguishable representations for samples with different sentiments. Extensive experiments on three benchmark datasets indicate that Joyful achieved state-of-the-art (SOTA) performance compared to all baselines.