Audio Visual Fusion

Audio-visual fusion integrates audio and visual data to improve performance in various tasks, primarily aiming to leverage the complementary strengths of each modality for more robust and accurate results. Current research heavily utilizes deep learning architectures, including transformers and convolutional neural networks, often incorporating attention mechanisms for effective multimodal feature fusion and employing techniques like late, intermediate, and hybrid fusion strategies. This field is significantly impacting applications such as emotion recognition, speaker verification, action recognition, and content moderation, offering improved accuracy and robustness over unimodal approaches. The development of efficient and effective fusion methods remains a key focus, particularly in addressing challenges posed by inconsistent or weakly complementary relationships between audio and visual data.