Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Abstract
This paper proposes a new hybrid architecture that consists of a deep Convolu-tional Network and a Markov Random Field. We show how this architecture is successfully applied to the challenging problem of articulated human pose estimation in monocular images. The architecture can exploit structural domain constraints such as geometric relationships between body joint locations. We show that joint training of these two model paradigms improves...
Paper Details
Title
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Published Date
Dec 8, 2014
Volume
27
Pages
1799 - 1807
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