Multi-modal Learning for Fusion of Heterogeneous Data: Investigating multi-modal learning approaches for integrating heterogeneous data sources, such as text, images, and sensor data

Abstract

Multi-modal learning has emerged as a powerful paradigm for handling the complexity and richness of modern data sources. This paper explores the fusion of heterogeneous data types, including text, images, and sensor data, through multi-modal learning approaches. We investigate the challenges and opportunities in integrating these data modalities and review state-of-the-art techniques for multi-modal fusion. We also discuss applications of multi-modal learning in various domains, highlighting its potential for enhancing data analysis and decision-making processes.