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Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in realworld applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative representation. It is unsupervised in the sense that the target dataset is completely unlabelled. Specifically, we present an multi-task
doi:10.1109/cvpr.2016.146
dblp:conf/cvpr/PengXWPGHT16
fatcat:u56wbkzymve27l4mmcepzcglh4