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As humans, we can remember certain visuals in great detail, and sometimeseven after viewing them once. What is even more interesting is that humans tendto remember and forget the same things, suggesting that there might be somegeneral internal characteristics of an image to encode and discard similartypes of information. Research suggests that some pictures tend to be memorizedmore than others. The ability of an image to be remembered by different viewersis one of its intrinsic properties. In visualization and photography, creatingmemorable images is a difficult task. Hence, to solve the problem, varioustechniques predict visual memorability and manipulate images' memorability. Wepresent a comprehensive literature survey to assess the deep learningtechniques used to predict and modify memorability. In particular, we analyzethe use of Convolutional Neural Networks, Recurrent Neural Networks, andGenerative Adversarial Networks for image memorability prediction andmodification.

Comprehensive Literature Survey on Deep Learning used in Image Memorability Prediction and Modification