  Understanding visual scenes relies more and more on dense pixel-wise
classification obtained via deep fully convolutional neural networks. However,
due to the nature of the networks, predictions often suffer from blurry
boundaries and ill-segmented shapes, fueling the need for post-processing. This
work introduces a new semantic segmentation regularization based on the
regression of a distance transform. After computing the distance transform on
the label masks, we train a FCN in a multi-task setting in both discrete and
continuous spaces by learning jointly classification and distance regression.
This requires almost no modification of the network structure and adds a very
low overhead to the training process. Learning to approximate the distance
transform back-propagates spatial cues that implicitly regularizes the
segmentation. We validate this technique with several architectures on various
datasets, and we show significant improvements compared to competitive
baselines.
