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Re-identify people in new domains with unsupervised learning by rewinding video with CycAs

Re-identify people in new domains with unsupervised learning by rewinding video with CycAs


Generalizable Re-Identification from Videos with Cycle Association

arXiv paper abstract https://arxiv.org/abs/2211.03663



... interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos.


... aim to learn a representation in an unsupervised manner and directly use the learned representation for re-ID in novel domains.


... First ... propose Cycle Association (CycAs), a scalable self-supervised learning method for re-ID with low training complexity ... second ... construct a large-scale unlabeled re-ID dataset named LMP-video


... CycAs learns re-ID features by enforcing cycle consistency of instance association between temporally successive video frame pairs, and the training cost is merely linear to the data size, making large-scale training possible.


... Trained on LMP-video, ... show that CycAs learns good generalization towards novel domains.


... sometimes even outperform supervised domain generalizable models ... CycAs ... surpassing state-of-the-art supervised DG re-ID methods ...



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