Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

• URL: http://arxiv.org/abs/2012.09071v2
• Date: Tue, 30 Mar 2021 10:52:24 GMT
• Title: Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
• Authors: Hao Chen, Yaohui Wang, Benoit Lagadec, Antitza Dantcheva, Francois Bremond
• Abstract summary: Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework.
• Score: 15.486689594217273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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