  We consider the problem of weakly supervised object detection, where the
training samples are annotated using only image-level labels that indicate the
presence or absence of an object category. In order to model the uncertainty in
the location of the objects, we employ a dissimilarity coefficient based
probabilistic learning objective. The learning objective minimizes the
difference between an annotation agnostic prediction distribution and an
annotation aware conditional distribution. The main computational challenge is
the complex nature of the conditional distribution, which consists of terms
over hundreds or thousands of variables. The complexity of the conditional
distribution rules out the possibility of explicitly modeling it. Instead, we
exploit the fact that deep learning frameworks rely on stochastic optimization.
This allows us to use a state of the art discrete generative model that can
provide annotation consistent samples from the conditional distribution.
Extensive experiments on PASCAL VOC 2007 and 2012 data sets demonstrate the
efficacy of our proposed approach.
