| Summary: | 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 artifacts in multimedia. It surveys the recent, authoritative, and the best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. This paper also discusses the challenges that the DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis.
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