Title: End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks,
Abstract: Recent years have seen a sharp increase in the number of related yet distinct
advances in semantic segmentation. Here, we tackle this problem by leveraging
the respective strengths of these advances. That is, we formulate a conditional
random field over a four-connected graph as end-to-end trainable convolutional
and recurrent networks, and estimate them via an adversarial process.
Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order
inconsistencies. We evaluate our model on two standard benchmark datasets for
semantic face segmentation, achieving state-of-the-art results on both of them.