Multi-Modal Learning: Combining Text, Image, and Audio Data

A significant research field is multi-modal learning, which facilitates the integration of multiple data modalities (text, image), audio or video to enhance model performance across different contexts. The use of complementary information from multiple sources in multi- modal systems can improve decision-making accuracy and robustness, unlike unimodal data systems that rely on a single data type. In this paper, we explore the methodologies and challenges of multi-modal learning, highlighting its potential as a transformative tool for various fields such as health care, education, and autonomous systems. The presentation includes a comprehensive examination of feature extraction techniques, fusion strategies, and modern architectures, along with an exploration of data alignment challenges, missing modalities, computational complexity, etc. By addressing these challenges, multi-modal learning is poised to become a major player in the future of artificial intelligence and data science.