Audio Visual Representation

Audio-visual representation learning aims to create computational models that understand the combined information from audio and visual data, mirroring human multi-sensory perception. Current research focuses on developing robust and efficient models, often employing transformer-based architectures and contrastive learning, to improve tasks like sound localization, video event detection, and speech recognition. These advancements are driven by the need for more accurate and generalized audio-visual understanding, impacting applications ranging from robotics and assistive technologies to multimedia analysis and content creation. The field is also exploring self-supervised learning techniques to reduce reliance on large, manually labeled datasets.