  Learning to localize objects with minimal supervision is an important problem
in computer vision, since large fully annotated datasets are extremely costly
to obtain. In this paper, we propose a new method that achieves this goal with
only image-level labels of whether the objects are present or not. Our approach
combines a discriminative submodular cover problem for automatically
discovering a set of positive object windows with a smoothed latent SVM
formulation. The latter allows us to leverage efficient quasi-Newton
optimization techniques. Our experiments demonstrate that the proposed approach
provides a 50% relative improvement in mean average precision over the current
state-of-the-art on PASCAL VOC 2007 detection.
