Multi-Modal Learning

Multi-modal learning is a machine learning approach that aims to enhance the model's representation and generalization capabilities by integrating multiple types of data (such as text, images, and audio). Its core objective is to achieve cross-modal information fusion and interaction, thereby providing a more comprehensive understanding and processing of complex real-world tasks. Multi-modal learning has broad application value in fields such as natural language processing, computer vision, and speech recognition, and can significantly improve the accuracy and robustness of systems.