  One of the most common problems of weakly supervised object localization is
that of inaccurate object coverage. In the context of state-of-the-art methods
based on Class Activation Mapping, this is caused either by localization maps
which focus, exclusively, on the most discriminative region of the objects of
interest or by activations occurring in background regions. To address these
two problems, we propose two representation regularization mechanisms: Full
Region Regularizationwhich tries to maximize the coverage of the localization
map inside the object region, and Common Region Regularization which minimizes
the activations occurring in background regions. We evaluate the two
regularizations on the ImageNet, CUB-200-2011 and OpenImages-segmentation
datasets, and show that the proposed regularizations tackle both problems,
outperforming the state-of-the-art by a significant margin.
