Multi Modal Representation Learning

Multi-modal representation learning aims to create unified representations from diverse data sources (e.g., images, text, audio) to improve the performance of downstream tasks. Current research focuses on developing effective fusion strategies, including transformer-based architectures and contrastive learning methods, to integrate information across modalities while addressing issues like modality imbalance and noise. These advancements are significantly impacting various fields, from medical diagnosis (e.g., Alzheimer's disease classification) and autonomous driving to improved molecular property prediction and enhanced search capabilities in e-commerce. The resulting richer, more robust representations are proving crucial for improving accuracy and generalizability in numerous applications.