@Misc{UmutGuclu2017,
author="Umut Guclu
and Yagmur Gucluturk
and Meysam Madadi
and Sergio Escalera
and Xavier Baro
and Jordi Gonzalez
and Rob van Lier
and Marcel A. J. van Gerven",
title="End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks",
year="2017",
abstract="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.",
optnote="HuPBA; ISE; 600.098; 600.119",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2932), last updated on Fri, 19 Feb 2021 09:38:58 +0100",
file=":http://refbase.cvc.uab.es/files/GGM2017.pdf:PDF"
}