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.

本文研究跨域图像检索的无监督情况，提出了一种新的无对应关系的跨域对齐方法，即不考虑对应关系和类别注释的情况下，利用 In-domain 自匹配监督机制和跨域分类器对齐消除跨域差异，实现了无监督跨域图像检索。