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Occlusion poses a significant difficulty for object recognition due to the combinatorial diversity of possible occlusion patterns. We take a strongly supervised, nonparametric approach to modeling occlusion by learning deformable models with many local part mixture templates using large quantities of synthetically generated training data. This allows the model to learn the appearance of different occlusion patterns including figure-ground cues such as the shapes of occluding contours as well asdoi:10.1109/cvpr.2014.308 dblp:conf/cvpr/GhiasiYRF14 fatcat:3xreaqfrmbeafbnl6ni5yy4m3e