  The cross-depiction problem refers to the task of recognising visual objects
regardless of their depictions; whether photographed, painted, sketched, {\em
etc}. In the past, some researchers considered cross-depiction to be domain
adaptation (DA). More recent work considers cross-depiction as domain
generalisation (DG), in which algorithms extend recognition from one set of
domains (such as photographs and coloured artwork) to another (such as
sketches). We show that fixing the last layer of AlexNet to random values
provides a performance comparable to state of the art DA and DG algorithms,
when tested over the PACS benchmark. With support from background literature,
our results lead us to conclude that texture alone is insufficient to support
generalisation; rather, higher-order representations such as structure and
shape are necessary.
