Audio Visual Model

Audio-visual models integrate audio and visual data to improve performance on various tasks, ranging from speech recognition and synthesis to deepfake detection and video understanding. Current research focuses on developing robust models, often employing transformer-based architectures and techniques like contrastive learning and iterative fine-tuning, to address challenges such as noisy environments, sparse data, and the need for efficient, lightweight systems. These advancements have significant implications for applications like improved human-computer interaction, enhanced multimedia content analysis, and more reliable detection of manipulated media.