  Weakly supervised semantic segmentation produces a pixel-level localization
from a classifier, but it is likely to restrict its focus to a small
discriminative region of the target object. AdvCAM is an attribution map of an
image that is manipulated to increase the classification score. This
manipulation is realized in an anti-adversarial manner, which perturbs the
images along pixel gradients in the opposite direction from those used in an
adversarial attack. It forces regions initially considered not to be
discriminative to become involved in subsequent classifications, and produces
attribution maps that successively identify more regions of the target object.
In addition, we introduce a new regularization procedure that inhibits the
incorrect attribution of regions unrelated to the target object and limits the
attributions of the regions that already have high scores. On PASCAL VOC 2012
test images, we achieve mIoUs of 68.0 and 76.9 for weakly and semi-supervised
semantic segmentation respectively, which represent a new state-of-the-art.
