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Semantics-Aligned Representation Learning for Person Re-identification
[article]
2020
arXiv
pre-print
Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build
arXiv:1905.13143v3
fatcat:sezzgtm32rhilixol3yamffwnm