A CNN cascade for landmark guided semantic part segmentation

Jackson, Aaron S. and Valstar, Michel and Tzimiropoulos, Georgios (2016) A CNN cascade for landmark guided semantic part segmentation. In: ECCV 2016 Workshop, Geometry meets Deep Learning, 9 October 2016, Amsterdam, Netherlands. (In Press)

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Abstract

This paper proposes a CNN cascade for semantic part segmentation guided by pose-specifc information encoded in terms of a set of landmarks (or keypoints). There is large amount of prior work on each of these tasks separately, yet, to the best of our knowledge, this is the first time in literature that the interplay between pose estimation and semantic part segmentation is investigated. To address this limitation of prior work, in this paper, we propose a CNN cascade of tasks that firstly performs landmark localisation and then uses this information as input for guiding semantic part segmentation. We applied our architecture to the problem of facial part segmentation and report large performance improvement over the standard unguided network on the most challenging face datasets. Testing code and models will be published online at http://cs.nott.ac.uk/~psxasj/.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: Proceedings of 14th European Conference on Computer Vision 2016, Workshop, Geometry meets Deep Learning. Lecture notes in computer science. Springer. The final publication is available at link.springer.com.
Keywords: pose estimation, landmark localisation, semantic part seg- mentation, faces
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Related URLs:
URLURL Type
http://www.eccv2016.org/UNSPECIFIED
https://sites.google.com/site/deepgeometry/UNSPECIFIED
Depositing User: Tzimiropoulos, Yorgos
Date Deposited: 29 Sep 2016 10:02
Last Modified: 01 Oct 2016 04:38
URI: http://eprints.nottingham.ac.uk/id/eprint/37234

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