Abstract
We present a semantic part detection approach that effectively leverages object information. We use the object
appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts
inside the objects based on their appearance. We achieve
this with a new network module, called OffsetNet, that effi-
ciently predicts a variable number of part locations within
a given object. Our model incorporates all these cues to
detect parts in the context of their objects. This leads to
considerably higher performance for the challenging task
of part detection compared to using part appearance alone
(+5 mAP on the PASCAL-Part dataset). We also compare
to other part detection methods on both PASCAL-Part and
CUB200-2011 datasets