Unsupervised Feature Representation Learning for Domain-generalized Cross-domain Image Retrieval

Cross-domain image retrieval has been extensively studied due to its high practical value. In recently proposed unsupervised cross-domain image retrieval methods, efforts are taken to break the data annotation barrier. However, applicability of the model is still confined to domains seen during training. This limitation motivates us to present the first attempt at domain-generalized unsupervised crossdomain image retrieval (DG-UCDIR) aiming at facilitating image retrieval between any two unseen domains in an unsupervised way. To improve domain generalizability of the model, we thus propose a new two-stage domain augmentation technique for diversified training data generation. DG-UCDIR also shares all the challenges present in the unsupervised cross-domain image retrieval, where domain-agnostic and semantic-aware feature representations are supposed to be learned without external supervision. To accomplish this, we introduce a novel crossdomain contrastive learning strategy by utilizing phase image as a proxy to mitigate the domain gap. Extensive experiments are carried out using PACS and Domain-Net dataset, and consistently illustrate the superior performance of our framework compared to existing state-ofthe-art methods. Our source code is available at https: //github.com/conghui1002/DG-UCDIR .