Cross-domain image retrieval aims at retrieving images across different domains to excavate cross-domain classificatory or correspondence relationships. This paper studies a less-touched problem of cross-domain image retrieval, i.e., unsupervised cross-domain image retrieval, considering the following practical assumptions: (i) no correspondence relationship, and (ii) no category annotations. It is challenging to align and bridge distinct domains without cross-domain correspondence. To tackle the challenge, we present a novel Correspondence-free Domain Alignment (CoDA) method to effectively eliminate the cross-domain gap through In-domain Self-matching Supervision (ISS) and Cross-domain Classifier Alignment (CCA). To be specific, ISS is presented to encapsulate discriminative information into the latent common space by elaborating a novel self-matching supervision mechanism. To alleviate the cross-domain discrepancy, CCA is proposed to align distinct domain-specific classifiers. Thanks to the ISS and CCA, our method could encode the discrimination into the domain-invariant embedding space for unsupervised cross-domain image retrieval. To verify the effectiveness of the proposed method, extensive experiments are conducted on four benchmark datasets compared with six state-of-the-art methods.

翻译：跨域图像检索旨在检索不同域之间的图像，以挖掘跨域的分类或对应关系。本文研究了一个较少被触及的跨域图像检索问题，即无监督跨域图像检索，基于以下实际假设：（i）无对应关系，以及（ii）无类别标注。在没有跨域对应关系的情况下对齐和桥接不同域具有挑战性。为应对这一挑战，我们提出了一种新颖的无对应关系域对齐（CoDA）方法，通过域内自匹配监督（ISS）和跨域分类器对齐（CCA）有效消除跨域差距。具体而言，ISS通过设计一种新颖的自匹配监督机制，将判别性信息封装到潜在公共空间中。为缓解跨域差异，CCA被提出用于对齐不同域的特定分类器。得益于ISS和CCA，我们的方法能够将判别性编码到域不变嵌入空间中，以实现无监督跨域图像检索。为验证所提方法的有效性，在四个基准数据集上进行了大量实验，并与六种最先进方法进行了比较。