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We propose hinge-loss Markov random fields (HL-MRFs), a powerful class of continuous-valued graphical models, for high-level computer vision tasks. HL-MRFs are characterized by log-concave density functions, and are able to perform efficient, exact inference. Their templated hinge-loss potential functions naturally encode soft-valued logical rules. Using the declarative modeling language probabilistic soft logic, one can easily define HL-MRFs via familiar constructs from first-order logic. Wedoi:10.1109/cvprw.2013.157 dblp:conf/cvpr/LondonKBHGD13 fatcat:ee6bi3pdmndttljkyx2ugrcxhu