Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
This work addresses the challenge of unsupervised person re-identification for computer vision researchers, offering a more flexible approach by not relying on labeled source datasets.
This paper tackles unsupervised person re-identification by combining a Generative Adversarial Network (GAN) and a contrastive learning module. The GAN provides online data augmentation, while the contrastive module learns view-invariant features for generation, leading to significant outperformance over state-of-the-art methods on large-scale ReID datasets.
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.