  Semantic segmentation is a task that traditionally requires a large dataset
of pixel-level ground truth labels, which is time-consuming and expensive to
obtain. Recent advancements in the weakly-supervised setting show that
reasonable performance can be obtained by using only image-level labels.
Classification is often used as a proxy task to train a deep neural network
from which attention maps are extracted. However, the classification task needs
only the minimum evidence to make predictions, hence it focuses on the most
discriminative object regions. To overcome this problem, we propose a novel
formulation of adversarial erasing of the attention maps. In contrast to
previous adversarial erasing methods, we optimize two networks with opposing
loss functions, which eliminates the requirement of certain suboptimal
strategies; for instance, having multiple training steps that complicate the
training process or a weight sharing policy between networks operating on
different distributions that might be suboptimal for performance. The proposed
solution does not require saliency masks, instead it uses a regularization loss
to prevent the attention maps from spreading to less discriminative object
regions. Our experiments on the Pascal VOC dataset demonstrate that our
adversarial approach increases segmentation performance by 2.1 mIoU compared to
our baseline and by 1.0 mIoU compared to previous adversarial erasing
approaches.
