  We present a technique for weakly supervised object localization (WSOL),
building on the observation that WSOL algorithms usually work better on images
with bigger objects. Instead of training the object detector on the entire
training set at the same time, we propose a curriculum learning strategy to
feed training images into the WSOL learning loop in an order from images
containing bigger objects down to smaller ones. To automatically determine the
order, we train a regressor to estimate the size of the object given the whole
image as input. Furthermore, we use these size estimates to further improve the
re-localization step of WSOL by assigning weights to object proposals according
to how close their size matches the estimated object size. We demonstrate the
effectiveness of using size order and size weighting on the challenging PASCAL
VOC 2007 dataset, where we achieve a significant improvement over existing
state-of-the-art WSOL techniques.
