  We present an active detection model for localizing objects in scenes. The
model is class-specific and allows an agent to focus attention on candidate
regions for identifying the correct location of a target object. This agent
learns to deform a bounding box using simple transformation actions, with the
goal of determining the most specific location of target objects following
top-down reasoning. The proposed localization agent is trained using deep
reinforcement learning, and evaluated on the Pascal VOC 2007 dataset. We show
that agents guided by the proposed model are able to localize a single instance
of an object after analyzing only between 11 and 25 regions in an image, and
obtain the best detection results among systems that do not use object
proposals for object localization.
