  The recognition of human actions and the determination of human attributes
are two tasks that call for fine-grained classification. Indeed, often rather
small and inconspicuous objects and features have to be detected to tell their
classes apart. In order to deal with this challenge, we propose a novel
convolutional neural network that mines mid-level image patches that are
sufficiently dedicated to resolve the corresponding subtleties. In particular,
we train a newly de- signed CNN (DeepPattern) that learns discriminative patch
groups. There are two innovative aspects to this. On the one hand we pay
attention to contextual information in an origi- nal fashion. On the other
hand, we let an iteration of feature learning and patch clustering purify the
set of dedicated patches that we use. We validate our method for action clas-
sification on two challenging datasets: PASCAL VOC 2012 Action and Stanford 40
Actions, and for attribute recogni- tion we use the Berkeley Attributes of
People dataset. Our discriminative mid-level mining CNN obtains state-of-the-
art results on these datasets, without a need for annotations about parts and
poses.
