Mutual Cross-Attention in Dyadic Fusion Networks for Audio-Video Emotion Recognition

Multimodal emotion recognition is a challenging problem in the research fields of human-computer interaction and pattern recognition. How to efficiently find a common subspace among the heterogeneous multimodal data is still an open problem for audio-video emotion recognition. In this work, we propose an attentive audio-video fusion network in an emotional dialogue system to learn attentive contextual dependency, speaker information, and the interaction of audio-video modalities. We employ pre-trained models, wav2vec, and Distract your Attention Network, to extract high-level audio and video representations, respectively. By using weighted fusion based on a cross-attention module, the cross-modality encoder focuses on the inter-modality relations and selectively captures effective information among the audio-video modality. Specifically, bidirectional gated recurrent unit models capture long-term contextual information, explore speaker influence, and learn intra-and inter-modal interactions of the audio and video modalities in a dynamic manner. We evaluate the approach on the MELD dataset, and the experimental results show that the proposed approach achieves state-of-the-art performance on the dataset.