Multimedia Deep Learning

Abstract

By achieving breakthrough results on domains such as speech recognition, natural language processing, and computer vision, it is no surprise that deep neural networks are receiving a lot of attention these days. Specifically in the field of multimedia data analysis, there is a tremendous amount of multimedia big data that is being generated every day. Deep learning has the potential to overcome the issue of multimedia data having massive and heterogeneous characteristics that make it a challenge to store and analyze the data. This can be accomplished by allowing computers to easily and automatically extract features from unstructured data without the need to rely on human intervention. Although recent multimedia deep learning methods have achieved some remarkable results, deep learning challenges such as interpretability and generalization still make it difficult to be fit for critical decision-making tasks from fields such as medicine and defense.