  This paper presents the first attempt to learn semantic boundary detection
using image-level class labels as supervision. Our method starts by estimating
coarse areas of object classes through attentions drawn by an image
classification network. Since boundaries will locate somewhere between such
areas of different classes, our task is formulated as a multiple instance
learning (MIL) problem, where pixels on a line segment connecting areas of two
different classes are regarded as a bag of boundary candidates. Moreover, we
design a new neural network architecture that can learn to estimate semantic
boundaries reliably even with uncertain supervision given by the MIL strategy.
Our network is used to generate pseudo semantic boundary labels of training
images, which are in turn used to train fully supervised models. The final
model trained with our pseudo labels achieves an outstanding performance on the
SBD dataset, where it is as competitive as some of previous arts trained with
stronger supervision.
