Unsupervised Person Re-identification Based on Clustering and Domain-Invariant Network

摘要

Person re-identification (Re-ID) is a task which aims to determine whether a pedestrian in a camera has emerged in other cameras. Earlier works stress importance of the supervised learning methods, however, creating labels by hand is too slow and expensive. Hence, supervised methods are always limited in real-world applications. To address the problem, we propose a novel domain adaptation framework for unsupervised person Re-ID. First, target data are clustered and selected to add relative reliable supervised information for target domain. Second, a novel domain adaptive network is designed to decompose the representations to person-related and domain-related part. The former aims at learning domain-invariant and discriminative representation by a adversarial loss and a Re-ID loss with the label smoothing regularization. And the latter further improve a model's ability of extracting domain-invariant features by separating the domain unique features. What's more, during learning representation for target domain, a labeled source data not only is utilized to initialize the model but also participate in the training as a beneficial supervision information to generalize the Re-ID model. Experimental results on Market-1501 and DukeMTMC-reID evidence the superior performance of the proposed model over state-of-the-art methods.