Modality Exchange Network

Modality exchange networks aim to improve the performance of tasks involving multiple data types (modalities), such as images and text, by effectively integrating information across these modalities. Current research focuses on developing architectures that efficiently fuse information from varying numbers and types of modalities, often employing attention mechanisms, transformers, and generative models to handle missing data or modality discrepancies. These networks are proving valuable in diverse applications, including medical image analysis (e.g., brain tumor segmentation, retinogeniculate pathway segmentation), object tracking, and emotion recognition, where robust multimodal fusion enhances accuracy and reliability.