A survey of deep learning solutions for multimedia visual content analysis.

The increasing use of social media networks on handheld devices, especially smartphones
with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband
connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive
volumes of digital media, including images and videos. Such media is full of visual information and holds
immense value in today’s world. The volume of data involved calls for automated visual content analysis
systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep Learning (DL)
has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature
and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined
handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they
can achieve, once the network is trained, under practical constraints. This paper identifies eight problem
domains which require analysis of visual artefacts in multimedia. It surveys the recent, authoritative, and
best performing DL solutions and lists the datasets used in the development of these deep methods for the
identified types of visual analysis problems. The paper also discusses the challenges that DL solutions face
which can compromise their reliability, robustness, and accuracy for visual content analysis.