  Recent years have seen a rapid growth in new approaches improving the
accuracy of semantic segmentation in a weakly supervised setting, i.e. with
only image-level labels available for training. However, this has come at the
cost of increased model complexity and sophisticated multi-stage training
procedures. This is in contrast to earlier work that used only a single stage
$-$ training one segmentation network on image labels $-$ which was abandoned
due to inferior segmentation accuracy. In this work, we first define three
desirable properties of a weakly supervised method: local consistency, semantic
fidelity, and completeness. Using these properties as guidelines, we then
develop a segmentation-based network model and a self-supervised training
scheme to train for semantic masks from image-level annotations in a single
stage. We show that despite its simplicity, our method achieves results that
are competitive with significantly more complex pipelines, substantially
outperforming earlier single-stage methods.
