Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition
This work addresses the problem of multimodal emotion recognition for applications like social media analysis, but it is incremental as it builds on existing contrastive and adversarial methods.
The paper tackles the challenge of eliminating heterogeneity between modalities in multimodal emotion recognition by proposing AR-IIGCN, which uses adversarial representation and graph contrastive learning to improve feature representation, resulting in significant accuracy improvements on IEMOCAP and MELD datasets.
With the release of increasing open-source emotion recognition datasets on social media platforms and the rapid development of computing resources, multimodal emotion recognition tasks (MER) have begun to receive widespread research attention. The MER task extracts and fuses complementary semantic information from different modalities, which can classify the speaker's emotions. However, the existing feature fusion methods have usually mapped the features of different modalities into the same feature space for information fusion, which can not eliminate the heterogeneity between different modalities. Therefore, it is challenging to make the subsequent emotion class boundary learning. To tackle the above problems, we have proposed a novel Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive for Multimodal Emotion Recognition (AR-IIGCN) method. Firstly, we input video, audio, and text features into a multi-layer perceptron (MLP) to map them into separate feature spaces. Secondly, we build a generator and a discriminator for the three modal features through adversarial representation, which can achieve information interaction between modalities and eliminate heterogeneity among modalities. Thirdly, we introduce contrastive graph representation learning to capture intra-modal and inter-modal complementary semantic information and learn intra-class and inter-class boundary information of emotion categories. Specifically, we construct a graph structure for three modal features and perform contrastive representation learning on nodes with different emotions in the same modality and the same emotion in different modalities, which can improve the feature representation ability of nodes. Extensive experimental works show that the ARL-IIGCN method can significantly improve emotion recognition accuracy on IEMOCAP and MELD datasets.