  In this paper, we present a large scale unlabeled person re-identification
(Re-ID) dataset "LUPerson" and make the first attempt of performing
unsupervised pre-training for improving the generalization ability of the
learned person Re-ID feature representation. This is to address the problem
that all existing person Re-ID datasets are all of limited scale due to the
costly effort required for data annotation. Previous research tries to leverage
models pre-trained on ImageNet to mitigate the shortage of person Re-ID data
but suffers from the large domain gap between ImageNet and person Re-ID data.
LUPerson is an unlabeled dataset of 4M images of over 200K identities, which is
30X larger than the largest existing Re-ID dataset. It also covers a much
diverse range of capturing environments (eg, camera settings, scenes, etc.).
Based on this dataset, we systematically study the key factors for learning
Re-ID features from two perspectives: data augmentation and contrastive loss.
Unsupervised pre-training performed on this large-scale dataset effectively
leads to a generic Re-ID feature that can benefit all existing person Re-ID
methods. Using our pre-trained model in some basic frameworks, our methods
achieve state-of-the-art results without bells and whistles on four widely used
Re-ID datasets: CUHK03, Market1501, DukeMTMC, and MSMT17. Our results also show
that the performance improvement is more significant on small-scale target
datasets or under few-shot setting.
