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Treating graph structures of Markov random fields as unknown and estimating them jointly with labels have been shown to be useful for modeling human activity recognition and other related tasks. We propose two novel relaxations for solving this problem. The first is a linear programming (LP) relaxation, which is provably tighter than the existing LP relaxation. The second is a non-convex quadratic programming (QP) relaxation, which admits an efficient concave-convex procedure (CCCP). The CCCPdoi:10.1109/iccv.2019.01003 dblp:conf/iccv/WangLSKZ19 fatcat:xzbv3lad7behnj3w6tqazbcl5m