  In this paper we propose a unified framework for structured prediction with
latent variables which includes hidden conditional random fields and latent
structured support vector machines as special cases. We describe a local
entropy approximation for this general formulation using duality, and derive an
efficient message passing algorithm that is guaranteed to converge. We
demonstrate its effectiveness in the tasks of image segmentation as well as 3D
indoor scene understanding from single images, showing that our approach is
superior to latent structured support vector machines and hidden conditional
random fields.
