Multi Modal Masked Autoencoders

Multi-modal masked autoencoders (MMAEs) are self-supervised learning models designed to learn robust representations from diverse data types, such as images, point clouds, audio, and text, by reconstructing masked portions of the input. Current research focuses on developing MMAE architectures optimized for specific applications, including dynamic emotion recognition, medical image analysis, and autonomous driving, often incorporating techniques like contrastive learning and multi-task training to improve performance. The ability of MMAEs to learn transferable representations from unlabeled data makes them valuable for various downstream tasks, particularly in scenarios with limited labeled data, thereby advancing both fundamental understanding of multimodal data and practical applications across diverse fields.