# End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks Source: [End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:34:16.778Z Authors: Umut G\\"u\\c{c}l\\"u, Ya\\u{g}mur G\\"u\\c{c}l\\"ut\\"urk, Meysam Madadi, Sergio Escalera, Xavier Bar\\'o, Jordi Gonz\\'alez, Rob van Lier, Marcel A. J. van Gerven Published: 2017-03-09T15:48:22.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()