Comprehensive Literature Survey on Deep Learning used in Image Memorability Prediction and Modification
This is an incremental survey for researchers in computer vision and psychology, summarizing existing work on image memorability.
The paper presents a comprehensive literature survey on deep learning techniques for predicting and modifying image memorability, analyzing methods like CNNs, RNNs, and GANs without reporting new experimental results.
As humans, we can remember certain visuals in great detail, and sometimes even after viewing them once. What is even more interesting is that humans tend to remember and forget the same things, suggesting that there might be some general internal characteristics of an image to encode and discard similar types of information. Research suggests that some pictures tend to be memorized more than others. The ability of an image to be remembered by different viewers is one of its intrinsic properties. In visualization and photography, creating memorable images is a difficult task. Hence, to solve the problem, various techniques predict visual memorability and manipulate images' memorability. We present a comprehensive literature survey to assess the deep learning techniques used to predict and modify memorability. In particular, we analyze the use of Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks for image memorability prediction and modification.