Research Area: Machine Learning
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.
Keywords:
Deep Multimodal Learning
multiple data modalities
multimodal representation
Machine Learning
Deep Learning
Author(s) Name: Dhanesh Ramachandram; Graham W. Taylor
Journal name: IEEE Signal Processing Magazine
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/MSP.2017.2738401
Volume Information: ( Volume: 34, Issue: 6, Nov. 2017) Page(s): 96 - 108
