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AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
[article]
2018
arXiv
pre-print
In this paper, we propose a novel method called AlignedReID that extracts a global feature which is jointly learned with local features. Global feature learning benefits greatly from local feature learning, which performs an alignment/matching by calculating the shortest path between two sets of local features, without requiring extra supervision. After the joint learning, we only keep the global feature to compute the similarities between images. Our method achieves rank-1 accuracy of 94.4% on
arXiv:1711.08184v2
fatcat:d22zbqrbrvhcdnbaa5w47ylza4