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• W2043409787 abstract "Existing person re-identification methods conventionally rely on labelled pairwise data to learn a task-specific distance metric for ranking. The value of unlabelled gallery instances is generally overlooked. In this study, we show that it is possible to propagate the query information along the unlabelled data manifold in an unsupervised way to obtain robust ranking results. In addition, we demonstrate that the performance of existing supervised metric learning methods can be significantly boosted once integrated into the proposed manifold ranking-based framework. Extensive evaluation is conducted on three benchmark datasets." @default.
• W2043409787 created "2016-06-24" @default.
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• W2043409787 date "2013-09-01" @default.
• W2043409787 modified "2023-10-05" @default.
• W2043409787 title "Person re-identification by manifold ranking" @default.
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• W2043409787 doi "https://doi.org/10.1109/icip.2013.6738736" @default.
• W2043409787 hasPublicationYear "2013" @default.
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