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Self-supervised Agent Learning for Unsupervised Cross-Domain Person Re-identification.
IEEE Transactions on Image Processing ( IF 10.6 ) Pub Date : 2020-08-20 , DOI: 10.1109/tip.2020.3016869
Kongzhu Jiang , Tianzhu Zhang , Yongdong Zhang , Feng Wu , Yong Rui

Unsupervised person re-identification (Re-ID) has better scalability and practicability than supervised Re-ID in the actual deployment. However, it is difficult to learn a discriminative Re-ID model without annotations. To address the above issue, we propose an end-to-end Self-supervised Agent Learning (SAL) algorithm by exploiting a set of agents as a bridge to reduce domain gaps for unsupervised cross-domain person Re-ID. The proposed SAL model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit self-supervised learning for unsupervised person Re-ID. Second, our model has designed three effective learning mechanisms including supervised label learning in source domain, similarity consistency learning in target domain, and self-supervised learning in cross domain, which can learn domain-invariant yet discriminative representations through the principled lens of agent learning by reducing domain discrepancy adaptively. Extensive experimental results on three standard benchmarks demonstrate that the proposed SAL performs favorably against state-of-the-art unsupervised person Re-ID methods.

中文翻译:

用于无监督跨域人员重新识别的自监督代理学习。

在实际部署中,无监督人员重新识别(Re-ID)比受监督的Re-ID具有更好的可伸缩性和实用性。但是,要学习没有注释的具有区别性的Re-ID模型是很困难的。为了解决上述问题,我们提出了一种端到端的自我监督代理学习(SAL)算法,该算法通过利用一组代理作为桥梁来减少无监督跨域人员Re-ID的域差距。提出的SAL模型具有许多优点。首先,就我们所知,这是为无监督人Re-ID开发自我监督学习的第一项工作。其次,我们的模型设计了三种有效的学习机制,包括源域中的监督标签学习,目标域中的相似性一致性学习和跨域中的自我监督学习,它可以通过代理学习的原理镜头通过自适应地减少领域差异来学习领域不变但具有区别性的表示形式。在三个标准基准上的大量实验结果表明,与最先进的无人监督Re-ID方法相比,拟议的SAL表现出色。
更新日期:2020-08-28
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