  When describing images, humans tend not to talk about the obvious, but rather
mention what they find interesting. We argue that abnormalities and deviations
from typicalities are among the most important components that form what is
worth mentioning. In this paper we introduce the abnormality detection as a
recognition problem and show how to model typicalities and, consequently,
meaningful deviations from prototypical properties of categories. Our model can
recognize abnormalities and report the main reasons of any recognized
abnormality. We introduce the abnormality detection dataset and show
interesting results on how to reason about abnormalities.
