Hybrid Multi-Attention Network for Audio–Visual Emotion Recognition Through Multimodal Feature Fusion

Multimodal emotion recognition involves leveraging complementary relationships across modalities to enhance the assessment of human emotions. Networks that integrate diverse information sources outperform single-modal approaches while offering greater robustness against noisy or missing data. Current emotion recognition approaches often rely on cross-modal attention mechanisms, particularly audio and visual modalities; however, these methods do not assume the complementary nature of the data. Despite making this assumption, it is not uncommon to see non-complementary relationships arise in real-world data, reducing the effectiveness of feature integration that assumes consistent complementarity. While audio–visual co-learning provides a broader understanding of contextual information for practical implementation, discrepancies between audio and visual data, such as semantic inconsistencies, pose challenges and lay the groundwork for inaccurate predictions. In this way, they have limitations in modeling the intramodal and cross-modal interactions. In order to address these problems, we propose a multimodal learning framework for emotion recognition, called the Hybrid Multi-ATtention Network (HMATN). Specifically, we introduce a collaborative cross-attentional paradigm for audio–visual amalgamation, intending to effectively capture salient features over modalities while preserving both intermodal and intramodal relationships. The model calculates cross-attention weights by analyzing the relationship between combined feature illustrations and distinct modes. Meanwhile, the network employs the Hybrid Attention of Single and Parallel Cross-Modal (HASPCM) mechanism, comprising a single-modal attention component and a parallel cross-modal attention component, to harness complementary multimodal data and hidden features to improve representation. Additionally, these modules exploit complementary and concealed multimodal information to enhance the richness of feature representation. Finally, the efficiency of the proposed method is demonstrated through experiments on complex videos from the AffWild2 and AFEW-VA datasets. The findings of these tests show that the developed attentional audio–visual fusion model offers a cost-efficient solution that surpasses state-of-the-art techniques, even when the input data are noisy or missing modalities.