Deep Multimodal Learning: A Survey on Recent Advances and Trends

Abstract

The success of deep learning has been a catalyst to solving increasingly complex machine-learning problems, which often involve multiple data modalities. We review recent advances in deep multimodal learning and highlight the state-of the art, as well as gaps and challenges in this active research field. We first classify deep multimodal learning architectures and then discuss methods to fuse learned multimodal representations in deep-learning architectures. We highlight two areas of research-regularization strategies and methods that learn or optimize multimodal fusion structures-as exciting areas for future work.