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Structural support vector machines (SSVMs) are amongst the best performing methods for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called max-oracle), which has to solve an optimization problem itself, e.g. a graph cut.
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Shah et al. (2015) studied this question.
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