PT Unknown
AU Umut Guclu
   Yagmur Gucluturk
   Meysam Madadi
   Sergio Escalera
   Xavier Baro
   Jordi Gonzalez
   Rob van Lier
   Marcel A. J. van Gerven
TI End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
PY 2017
AB arXiv:1703.03305 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 pairwisepotentials, 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.
ER