A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning

TL;DR

This paper introduces a two-stage multimodal emotion recognition model utilizing graph contrastive learning to better capture inter- and intra-modal emotional features, leading to improved accuracy in human-computer interaction scenarios.

Contribution

It proposes a novel two-stage model with graph contrastive learning for multimodal emotion recognition, addressing limitations of single-round classification and feature fusion.

Findings

Superior performance on IEMOCAP dataset

Outperforms previous methods on MELD dataset

Effective in capturing modal similarities and differences

Abstract

In terms of human-computer interaction, it is becoming more and more important to correctly understand the user's emotional state in a conversation, so the task of multimodal emotion recognition (MER) started to receive more attention. However, existing emotion classification methods usually perform classification only once. Sentences are likely to be misclassified in a single round of classification. Previous work usually ignores the similarities and differences between different morphological features in the fusion process. To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL). First, we encode the original dataset with different preprocessing modalities. Second, a graph contrastive learning (GCL) strategy is introduced for these three modal data with other structures to learn similarities and differences within and between modalities. Finally, we use MLP twice to achieve the final emotion classification. This staged classification method can help the model to better focus on different levels of emotional information, thereby improving the performance of the model. Extensive experiments show that TS-GCL has superior performance on IEMOCAP and MELD datasets compared with previous methods.