  Pixel-level annotations are expensive and time consuming to obtain. Hence,
weak supervision using only image tags could have a significant impact in
semantic segmentation. Recently, CNN-based methods have proposed to fine-tune
pre-trained networks using image tags. Without additional information, this
leads to poor localization accuracy. This problem, however, was alleviated by
making use of objectness priors to generate foreground/background masks.
Unfortunately these priors either require training pixel-level
annotations/bounding boxes, or still yield inaccurate object boundaries. Here,
we propose a novel method to extract markedly more accurate masks from the
pre-trained network itself, forgoing external objectness modules. This is
accomplished using the activations of the higher-level convolutional layers,
smoothed by a dense CRF. We demonstrate that our method, based on these masks
and a weakly-supervised loss, outperforms the state-of-the-art tag-based
weakly-supervised semantic segmentation techniques. Furthermore, we introduce a
new form of inexpensive weak supervision yielding an additional accuracy boost.
