Perhaps the most intriguing, but at the same time the most ambiguous, topic in the context of computational analysis of
art is the one related to perception. Different aspects of visual perception have been studied by psychologists for a long
time and have in the recent years become an rising subject of interest within the computer vision and deep learning
community. In particular, computational aesthetics is a growing ﬁeld preoccupied with developing computational
methods that can predict aesthetic judgments in a similar manner as humans. Developing quantitative methods for
analyzing subjective aspects of perception is particularly challenging in the context of art images. One of the major
challenges in studying perceptual characteristics of images is the development of large-scale datasets annotated with
evaluation scores obtained using experimental surveys. Amirshahi et al. introduced the JenAesthetics dataset [6], a
UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK