  We introduce a new loss function for the weakly-supervised training of
semantic image segmentation models based on three guiding principles: to seed
with weak localization cues, to expand objects based on the information about
which classes can occur in an image, and to constrain the segmentations to
coincide with object boundaries. We show experimentally that training a deep
convolutional neural network using the proposed loss function leads to
substantially better segmentations than previous state-of-the-art methods on
the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the
working mechanism of our method by a detailed experimental study that
illustrates how the segmentation quality is affected by each term of the
proposed loss function as well as their combinations.
