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Unsupervised Tracklet Person Re-Identification
[article]
2019
arXiv
pre-print
Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in a practical re-id deployment, due to the lack of exhaustive identity labelling of positive and negative image pairs for every camera-pair. In this work, we present an unsupervised re-id deep learning approach. It is capable of incrementally discovering and exploiting the underlying re-id discriminative information
arXiv:1903.00535v1
fatcat:b2y3min6ezhwrayhvtvo2pf27m