Modality Fusion

Modality fusion aims to combine information from multiple data sources (e.g., images, text, audio) to improve the performance and robustness of machine learning models. Current research focuses on developing effective fusion strategies, often employing transformer networks, graph neural networks, or state space models, and exploring optimal fusion points within model architectures to address issues like modality misalignment and incomplete data. This field is significant because it enables more comprehensive and accurate analysis of complex data, with applications ranging from improved medical diagnosis and plant identification to enhanced human-computer interaction and video understanding.