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This paper presents a novel approach to pedestrian classification which involves utilizing the synthesized virtual samples of a learned generative model to enhance the classification performance of a discriminative model. Our generative model captures prior knowledge about the pedestrian class in terms of a number of probabilistic shape and texture models, each attuned to a particular pedestrian pose. Active learning provides the link between the generative and discriminative model, in thedoi:10.1109/cvpr.2008.4587592 dblp:conf/cvpr/EnzweilerG08 fatcat:t2s6tmys2jdhtezqsnrt3oc2ru