  Large-scale training for semantic segmentation is challenging due to the
expense of obtaining training data for this task relative to other vision
tasks. We propose a novel training approach to address this difficulty. Given
cheaply-obtained sparse image labelings, we propagate the sparse labels to
produce guessed dense labelings. A standard CNN-based segmentation network is
trained to mimic these labelings. The label-propagation process is defined via
random-walk hitting probabilities, which leads to a differentiable
parameterization with uncertainty estimates that are incorporated into our
loss. We show that by learning the label-propagator jointly with the
segmentation predictor, we are able to effectively learn semantic edges given
no direct edge supervision. Experiments also show that training a segmentation
network in this way outperforms the naive approach.
