LAUSR

Clothing changes and lack of data labels are both crucial challenges in person ReID. For the former challenge, people may occur multiple times wearing different clothes. A majority of current research works for person ReID focuses on benchmarks in which a person’s clothing is kept the same all the time. For the latter challenge, most researchers try to transfer information from a labeled source dataset to an unlabeled target dataset. Whereas purely unsupervised training is less used. In this letter, we aim to solve both problems simultaneously. We design a novel unsupervised model, Syn-Person-Cluster ReID, to solve the unlabeled clothing change person ReID problem. We develop a purely unsupervised pipeline equipped with two innovations: synthetic augmentation on person images and feature restriction for the same person. The synthetic augmentation is to supply additional information for the same person, which can then be used as partially supervised inputs to the feature restriction. Extensive experiments on clothing change ReID datasets show the out-performance of our methods.