  Semantic segmentation models are limited in their ability to scale to large
numbers of object classes. In this paper, we introduce the new task of
zero-shot semantic segmentation: learning pixel-wise classifiers for never-seen
object categories with zero training examples. To this end, we present a novel
architecture, ZS3Net, combining a deep visual segmentation model with an
approach to generate visual representations from semantic word embeddings. By
this way, ZS3Net addresses pixel classification tasks where both seen and
unseen categories are faced at test time (so called "generalized" zero-shot
classification). Performance is further improved by a self-training step that
relies on automatic pseudo-labeling of pixels from unseen classes. On the two
standard segmentation datasets, Pascal-VOC and Pascal-Context, we propose
zero-shot benchmarks and set competitive baselines. For complex scenes as ones
in the Pascal-Context dataset, we extend our approach by using a graph-context
encoding to fully leverage spatial context priors coming from class-wise
segmentation maps.
