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Recently several generalizations of the popular latent structural SVM framework have been proposed in the literature. Broadly speaking, the generalizations can be divided into two categories: (i) those that predict the output variables while either marginalizing the latent variables or estimating their most likely values; and (ii) those that predict the output variables by minimizing an entropy-based uncertainty measure over the latent space. In order to aid their application in computerdoi:10.1109/iccv.2015.334 dblp:conf/iccv/BouchacourtNK15 fatcat:r3rvkdnn5zhillkorrqk7qotli