Abstract:
Although existing person Re-IDentification (ReID) methods have achieved great progress with large-scale labeled data, it is still hard to generalize to unseen scenarios w...Show MoreMetadata
Abstract:
Although existing person Re-IDentification (ReID) methods have achieved great progress with large-scale labeled data, it is still hard to generalize to unseen scenarios without any la-beled person identities. To alleviate this problem, this paper proposes a new framework to take full advantage of the label information of source domain and the data distribution geometry of unlabeled target domain to improve the ReID performance in the unlabeled target domain. Instead of direct model transfer, the data transfer is first adopted where the identity preserving samples are generated from the labeled source domain to unlabeled target domain. Accordingly, a better initialized target domain adapted ReID model could be obtained with the generated samples. The fine-grained part-level features are then learned instead of global features to better mine new persons in the unlabeled target domain. Finally, the proposed framework iteratively updates the ReID model with the generated persons and the mined persons in last iteration, and explores new persons from the unlabeled target domain. The state-of-the-art experimental results are achieved on Market1501 and DukeMTMC-reID in terms of unsupervised cross-domain person ReID.
Date of Conference: 22-25 September 2019
Date Added to IEEE Xplore: 26 August 2019
ISBN Information: