Given a query sentence, text-to-image person retrieval aims to identify matched pedestrian images from a large gallery. Most of the existing methods are designed for the unified domain setting, which is operated under the assumption that the training and test data are drawn from the same distribution. However, this assumption is difficult to guarantee in real application scenes, as data is often collected from various surveillance scenarios. To this end, in this paper, we introduce the concept of single-source domain generalization into the context of text-to-image person retrieval and propose a novel task called single-source domain generalizable text-to-image person retrieval (SSDG-TIPR). This task is applicable in real-world scenarios but poses significant challenges due to the limitation of accessible training data. Intuitively, a trained model is the most familiar with the domain on which it was trained, that is, the source domain. Therefore, to handle this SSDG-TIPR task, we propose a new method to infinitely close astray features from unseen target domains to the source domain, namely, to take it home (TIME), allowing the model to handle the features in a familiar manner. The proposed TIME method comprises three main modules: the Domain Astray Leading (DAL) module, the Domain Invariant Feature Extract (DIFE) module and the Domain Home Taking (DoT) module. We evaluated TIME on 3 benchmark datasets, namely CUHK-PEDES, ICFG-PEDES and RSTPReid, and demonstrated its superior performance on 10 SSDG-TIPR sub-tasks as well as on 3 conventional TIPR sub-tasks, establishing a new state-of-the-art in both settings.
Dong et al. (2026) studied this question.