Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification
Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification
Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achieved remarkable accuracy in settled scenes, i.e., source domain, few works can generalize well on the unseen target domain. One popular solution is assigning unlabeled …