  High annotation costs are a major bottleneck for the training of semantic
segmentation systems. Therefore, methods working with less annotation effort
are of special interest. This paper studies the problem of semi-supervised
semantic segmentation. This means that only a small subset of the training
images is annotated while the other training images do not contain any
annotation. In order to leverage the information present in the unlabeled
images, we propose to learn a second task that is related to semantic
segmentation but easier. On labeled images, we learn latent classes consistent
with semantic classes so that the variety of semantic classes assigned to a
latent class is as low as possible. On unlabeled images, we predict a
probability map for latent classes and use it as a supervision signal to learn
semantic segmentation. The latent classes, as well as the semantic classes, are
simultaneously predicted by a two-branch network. In our experiments on Pascal
VOC and Cityscapes, we show that the latent classes learned this way have an
intuitive meaning and that the proposed method achieves state of the art
results for semi-supervised semantic segmentation.
