  Since the rise of deep learning, many computer vision tasks have seen
significant advancements. However, the downside of deep learning is that it is
very data-hungry. Especially for segmentation problems, training a deep neural
net requires dense supervision in the form of pixel-perfect image labels, which
are very costly. In this paper, we present a new loss function to train a
segmentation network with only a small subset of pixel-perfect labels, but take
the advantage of weakly-annotated training samples in the form of cheap
bounding-box labels. Unlike recent works which make use of box-to-mask proposal
generators, our loss trains the network to learn a label uncertainty within the
bounding-box, which can be leveraged to perform online bootstrapping (i.e.
transforming the boxes to segmentation masks), while training the network. We
evaluated our method on binary segmentation tasks, as well as a multi-class
segmentation task (CityScapes vehicles and persons). We trained each task on a
dataset comprised of only 18% pixel-perfect and 82% bounding-box labels, and
compared the results to a baseline model trained on a completely pixel-perfect
dataset. For the binary segmentation tasks, our method achieves an IoU score
which is ~98.33% as good as our baseline model, while for the multi-class task,
our method is 97.12% as good as our baseline model (77.5 vs. 79.8 mIoU).
