Deep Multimodal Learning: A Survey on Recent Advances and Trends
计算机科学
深度学习
人工智能
数据科学
作者
Dhanesh Ramachandram,Graham P. Taylor
出处
期刊:IEEE Signal Processing Magazine [Institute of Electrical and Electronics Engineers] 日期:2017-11-09卷期号:34 (6): 96-108被引量:351
标识
DOI:10.1109/msp.2017.2738401
摘要
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.