Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the quality of clustering depends heavily on the quality of the learned features, which are overwhelmingly dominated by the colors in images especially in the unsupervised setting. In this paper, we propose a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which cluster structure is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework. To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively. Extensive experiments conducted on three benchmark datasets demonstrate superior performance of our proposal.
翻译:无人监督的人重新身份识别(Re-ID)旨在将无人监督环境中不同相机观点的行人图像与无人监督环境中不同相机观点的行人图像相匹配; 无人监督的人重新身份识别的现有方法通常建立在群集的假标签上; 然而,集群的质量在很大程度上取决于所学特征的质量,这些特征绝大多数以图像中的颜色为主,特别是在无人监督环境中。 在本文中,我们建议对无人监督的人重新身份识别采用分组引导非对称匹配学习(CACL)方法,其中集成结构被用来在一个设计得当的不对称对比学习框架内指导特征学习。 具体地说,我们建议采用一种新的集群级对比损失,以帮助Siamese网络有效地消除不同数据增强观点内部和之间在群集结构中进行特征学习时的偏差。 在三个基准数据集上进行的广泛实验显示了我们提案的优异性。