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Most existing person re-identification algorithms either extract robust visual features or learn discriminative metrics for person images. However, the underlying manifold which those images reside on is rarely investigated. That raises a problem that the learned metric is not smooth with respect to the local geometry structure of the data manifold. In this paper, we study person re-identification with manifold-based affinity learning, which did not receive enough attention from this area. Andoi:10.1109/cvpr.2017.358 dblp:conf/cvpr/BaiBT17 fatcat:moybzs6nwvc4nbauhbjkshnoqm