  We present Generative Semantic Segmentation (GSS), a generative learning
approach for semantic segmentation. Uniquely, we cast semantic segmentation as
an image-conditioned mask generation problem. This is achieved by replacing the
conventional per-pixel discriminative learning with a latent prior learning
process. Specifically, we model the variational posterior distribution of
latent variables given the segmentation mask. To that end, the segmentation
mask is expressed with a special type of image (dubbed as maskige). This
posterior distribution allows to generate segmentation masks unconditionally.
To achieve semantic segmentation on a given image, we further introduce a
conditioning network. It is optimized by minimizing the divergence between the
posterior distribution of maskige (i.e., segmentation masks) and the latent
prior distribution of input training images. Extensive experiments on standard
benchmarks show that our GSS can perform competitively to prior art
alternatives in the standard semantic segmentation setting, whilst achieving a
new state of the art in the more challenging cross-domain setting.
