Multimodal Self Supervised Learning

Multimodal self-supervised learning aims to learn robust representations from unlabeled data encompassing multiple modalities (e.g., images, text, audio). Current research focuses on developing effective strategies for aligning and fusing information across modalities, employing architectures like contrastive learning, masked autoencoders, and transformers to capture both shared and unique information. This approach is significant because it addresses the limitations of supervised learning by reducing reliance on expensive labeled datasets, enabling the development of more powerful and generalizable models across diverse applications like healthcare, remote sensing, and robotics.