  The goal of research in automatic image popularity assessment (IPA) is to
develop computational models that can accurately predict the potential of a
social image to go viral on the Internet. Here, we aim to single out the
contribution of visual content to image popularity, i.e., intrinsic image
popularity. Specifically, we first describe a probabilistic method to generate
massive popularity-discriminable image pairs, based on which the first
large-scale image database for intrinsic IPA (I$^2$PA) is established. We then
develop computational models for I$^2$PA based on deep neural networks,
optimizing for ranking consistency with millions of popularity-discriminable
image pairs. Experiments on Instagram and other social platforms demonstrate
that the optimized model performs favorably against existing methods, exhibits
reasonable generalizability on different databases, and even surpasses
human-level performance on Instagram. In addition, we conduct a psychophysical
experiment to analyze various aspects of human behavior in I$^2$PA.
