  Weakly Supervised Object Localization (WSOL) methods generate both
classification and localization results by learning from only image category
labels. Previous methods usually utilize class activation map (CAM) to obtain
target object regions. However, most of them only focus on improving foreground
object parts in CAM, but ignore the important effect of its background
contents. In this paper, we propose a confidence segmentation (ConfSeg) module
that builds confidence score for each pixel in CAM without introducing
additional hyper-parameters. The generated sample-specific confidence mask is
able to indicate the extent of determination for each pixel in CAM, and further
supervises additional CAM extended from internal feature maps. Besides, we
introduce Co-supervised Augmentation (CoAug) module to capture feature-level
representation for foreground and background parts in CAM separately. Then a
metric loss is applied at batch sample level to augment distinguish ability of
our model, which helps a lot to localize more related object parts. Our final
model, CSoA, combines the two modules and achieves superior performance, e.g.
$37.69\%$ and $48.81\%$ Top-1 localization error on CUB-200 and ILSVRC
datasets, respectively, which outperforms all previous methods and becomes the
new state-of-the-art.
