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Multi-Attention Fusion Network for Video-based Emotion Recognition

Humans routinely pay attention to important emotion information from visual and audio modalities without considering multimodal alignment issues, and recognize emotions by integrating important multimodal information at a certain interval. In this paper, we propose a multiple attention fusion network (MAFN) with the goal of improving emotion recognition performance by modeling human emotion recognition mechanisms. MAFN consists of two types of attention mechanisms: the intra-modality attention mechanism is applied to dynamically extract representative emotion features from a single modal frame sequences; the inter-modality attention mechanism is applied to automatically highlight specific modal features based on their importance. In addition, we define a multimodal domain adaptation method to have a positive effect on capturing interactions between modalities. MAFN achieved 58.65% recognition accuracy with the AFEW testing set, which is a significant improvement compared with the baseline of 41.07%.