Multi Modal Learning

Multi-modal learning aims to improve machine learning performance by integrating information from diverse data sources like images, text, and audio. Current research emphasizes developing robust methods for aligning and fusing these modalities, often employing techniques like contrastive learning, latent variable models, and attention mechanisms within various architectures including transformers and generative models. This field is significant because it enables more accurate and comprehensive analyses across numerous domains, from medical diagnosis (e.g., using images and genomic data) to action recognition (e.g., combining RGB and skeletal data), improving both scientific understanding and practical applications.