  Semantic segmentation has made much progress with increasingly powerful
pixel-wise classifiers and incorporating structural priors via Conditional
Random Fields (CRF) or Generative Adversarial Networks (GAN). We propose a
simpler alternative that learns to verify the spatial structure of segmentation
during training only. Unlike existing approaches that enforce semantic labels
on individual pixels and match labels between neighbouring pixels, we propose
the concept of Adaptive Affinity Fields (AAF) to capture and match the semantic
relations between neighbouring pixels in the label space. We use adversarial
learning to select the optimal affinity field size for each semantic category.
It is formulated as a minimax problem, optimizing our segmentation neural
network in a best worst-case learning scenario. AAF is versatile for
representing structures as a collection of pixel-centric relations, easier to
train than GAN and more efficient than CRF without run-time inference. Our
extensive evaluations on PASCAL VOC 2012, Cityscapes, and GTA5 datasets
demonstrate its above-par segmentation performance and robust generalization
across domains.
