A CNN Cascade for Landmark Guided Semantic Part Segmentation
This work addresses the interplay between pose estimation and semantic part segmentation, particularly for facial analysis, representing an incremental advance by combining existing tasks.
The paper tackles the problem of semantic part segmentation by integrating pose estimation through a CNN cascade that first localizes landmarks and then uses them to guide segmentation, reporting large performance improvements on challenging face datasets.
This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific 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/.