Current supervised cross-domain image retrieval methods can achieve excellent
performance. However, the cost of data collection and labeling imposes an
intractable barrier to practical deployment in real applications. In this
paper, we investigate the unsupervised cross-domain image retrieval task, where
class labels and pairing annotations are no longer a prerequisite for training.
This is an extremely challenging task because there is no supervision for both
in-domain feature representation learning and cross-domain alignment. We
address both challenges by introducing: 1) a new cluster-wise contrastive
learning mechanism to help extract class semantic-aware features, and 2) a
novel distance-of-distance loss to effectively measure and minimize the domain
discrepancy without any external supervision. Experiments on the Office-Home
and DomainNet datasets consistently show the superior image retrieval
accuracies of our framework over state-of-the-art approaches. Our source code
can be found at this https URL

本文探讨无监督的跨域图像检索任务，提出了基于类簇对比学习和距离 - 距离损失的方法，无需外部监督，在 Office-Home 和 DomainNet 数据集上实验结果表明，我们的方法优于现有最先进方法。