Existing person re-identification (ReID) methods typically directly load the
pre-trained ImageNet weights for initialization. However, as a fine-grained
classification task, ReID is more challenging and exists a large domain gap
between ImageNet classification. Inspired by the great success of
self-supervised representation learning with contrastive objectives, in this
paper, we design an Unsupervised Pre-training framework for ReID based on the
contrastive learning (CL) pipeline, dubbed UP-ReID. During the pre-training, we
attempt to address two critical issues for learning fine-grained ReID features:
(1) the augmentations in CL pipeline may distort the discriminative clues in
person images. (2) the fine-grained local features of person images are not
fully-explored. Therefore, we introduce an intra-identity
(I$^2$-)regularization in the UP-ReID, which is instantiated as two constraints
coming from global image aspect and local patch aspect: a global consistency is
enforced between augmented and original person images to increase robustness to
augmentation, while an intrinsic contrastive constraint among local patches of
each image is employed to fully explore the local discriminative clues.
Extensive experiments on multiple popular Re-ID datasets, including PersonX,
Market1501, CUHK03, and MSMT17, demonstrate that our UP-ReID pre-trained model
can significantly benefit the downstream ReID fine-tuning and achieve
state-of-the-art performance. Codes and models will be released to
this https URL

本文设计了一个基于对比学习管道的无监督预训练框架 UP-ReID，旨在解决细粒度 ReID 特征学习中的两个关键问题，并介绍了全局一致性和局部补丁之间的内在对比约束，在多个流行的 Re-ID 数据集上进行了广泛的实验，结果表明，我们的 UP-ReID 预训练模型可以显著改进下游 ReID 微调，并实现了最先进的性能。