Deep Classification Consistency for Person Re-Identification

Person re-identification (Re-ID) has greatly benefited from utilizing features of different levels. Most methods draw support from elegant network designs and elaborate fusion modules. In this paper, different from employing multi-level feature fusion like mainstream skip connection methods, we propose a Deep Classification Consistency(DCC) layer which imposes classification consistency on deep features from different stages in CNNs. DCC regularizes the training process of networks. It profoundly changes the distribution of learned features, making them more discriminative and transferable. Extensive experiments on Market-1501, DukeMTMC-reID, and CUHK03 datasets prove that the proposed method has obtained competitive performances when compared with state-of-the-art approaches, especially those advanced methods merely based on metric learning.