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Most existing person re-identification (ReID) methods assume the availability of extensively labelled cross-view person pairs and a closed-set scenario (i.e. all the probe people exist in the gallery set). These two assumptions significantly limit their usefulness and scalability in real-world applications, particularly with large scale camera networks. To overcome the limitations, we introduce a...
Owing to visual ambiguities and disparities, person re-identification methods inevitably produce sub optimal rank-list, which still requires exhaustive human eyeballing to identify the correct target from hundreds of different likely-candidates. Existing re-identification studies focus on improving the ranking performance, but rarely look into the critical problem of optimising the time-consuming...
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...
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