  The cross-depiction problem is that of recognising visual objects regardless
of whether they are photographed, painted, drawn, etc. It is a potentially
significant yet under-researched problem. Emulating the remarkable human
ability to recognise objects in an astonishingly wide variety of depictive
forms is likely to advance both the foundations and the applications of
Computer Vision.
  In this paper we benchmark classification, domain adaptation, and deep
learning methods; demonstrating that none perform consistently well in the
cross-depiction problem. Given the current interest in deep learning, the fact
such methods exhibit the same behaviour as all but one other method: they show
a significant fall in performance over inhomogeneous databases compared to
their peak performance, which is always over data comprising photographs only.
Rather, we find the methods that have strong models of spatial relations
between parts tend to be more robust and therefore conclude that such
information is important in modelling object classes regardless of appearance
details.
