Multi-modal Fusion Techniques in Deep Learning: Studying multi-modal fusion techniques for integrating information from diverse data sources in deep learning models

Abstract

Multi-modal fusion techniques play a crucial role in deep learning, enabling the integration of information from diverse data sources. This paper provides a comprehensive overview of the state-of-the-art multi-modal fusion techniques in deep learning, focusing on their applications, advantages, and challenges. We discuss various fusion strategies, including early, late, and hybrid fusion, and examine how they can be applied to different types of data, such as text, images, audio, and video. Additionally, we explore the impact of multi-modal fusion on improving model performance, enhancing interpretability, and enabling multimodal understanding. Finally, we highlight future research directions and open challenges in the field of multi-modal fusion in deep learning.