Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a mesh-based view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID datsets.

翻译：最近自我监督的对比式学习为不受监督的人通过学习不同观点(变异版本)的不同观点(变异版本)对输入进行重新识别(ReID)提供了一种有效的方法。 在本文中,我们将创用反向网络(GAN)和一个反向学习模块纳入一个联合培训框架。虽然GAN为对比性学习提供在线数据增强,但对比式模块为下一代学习视觉和变异特征。在此背景下,我们提议了一个基于网状的视图生成器。具体地说,网状预测作为生成一个人新观点的参考。此外,我们提出视觉反向损失,以便利原始观点和生成的观点之间的对比学习。我们偏离了以前基于GAN的未受监督的重新开发方法,涉及域适应,我们并不依赖标签的源数据集,从而使我们的方法更加灵活。广泛的实验结果表明,我们的方法大大超出了几个大尺度的ReID Datsect 的域适应环境。