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Deep Representation Learning with Part Loss for Person Re-Identification
2019
IEEE Transactions on Image Processing
Learning discriminative representations for unseen person images is critical for person Re-Identification (ReID). Most of current approaches learn deep representations in classification tasks, which essentially minimizes the empirical classification risk on the training set. As shown in our experiments, such representations easily get over-fitted on a discriminative human body part on the training set. To gain the discriminative power on unseen person images, we propose a deep representation
doi:10.1109/tip.2019.2891888
pmid:30629501
fatcat:sixozjalnnh6lmcfturmo4jese