Cross Modal

Cross-modal research focuses on integrating information from different data modalities (e.g., text, images, audio) to improve the performance of machine learning models. Current research emphasizes developing robust model architectures, such as contrastive masked autoencoders, diffusion models, and transformers, to effectively align and fuse these diverse data types, often addressing challenges like modality gaps and missing data through techniques like multi-graph alignment and cross-modal contrastive learning. This field is significant because it enables more comprehensive and accurate analysis of complex data, with applications ranging from medical diagnosis and video generation to misinformation detection and person re-identification.