Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain via distribution alignment. However, in real-world scenarios like public safety or access control, obtaining sufficient source data is challenging, limiting existing UDA methods. This paper investigates a realistic but rarely studied problem called one-shot unsupervised domain adaptation (OSUDA), where only one source example per category is available. OSUDA faces dual challenges in feature learning and domain alignment due to the extreme source data scarcity. To address these, we propose link-based contrastive learning (LCL), a simple yet effective approach for OSUDA. LCL leverages in-domain links to learn discriminative features from abundant unlabeled target data and cross-domain links to achieve precise domain alignment with only one source sample per category. Extensive experiments on four domain adaptation benchmarks (VisDA-2017, Office-31, Office-Home, and DomainNet) demonstrate LCL’s effectiveness under the OSUDA setting. Additionally, we construct a real-world OSUDA surveillance face recognition dataset, where LCL consistently improves recognition performance across various face recognition methods.

Online relevance feedback (RF) is widely utilized in instance search (INS) tasks to further refine imperfect ranking results, but it often has low interaction efficiency. The active learning (AL) technique addresses this problem by selecting valuable feedback candidates. However, mainstream AL methods require an initial labeled set for a cold start and are often computationally complex to solve. Therefore, they cannot fully satisfy the requirements for online RF in interactive INS tasks. To address this issue, we propose a confidence-aware active feedback method (CAAF) that is specifically designed for online RF in interactive INS tasks. Inspired by the explicit difficulty modeling scheme in self-paced learning, CAAF utilizes a pairwise manifold ranking loss to evaluate the ranking confidence of each unlabeled sample. The ranking confidence improves not only the interaction efficiency by indicating valuable feedback candidates but also the ranking quality by modulating the diffusion weights in manifold ranking. In addition, we design two acceleration strategies, an approximate optimization scheme and a top-$K$search scheme, to reduce the computational complexity of CAAF. Extensive experiments on both image INS tasks and video INS tasks searching for buildings, landscapes, persons, and human behaviors demonstrate the effectiveness of the proposed method. Notably, in the real-world, large-scale video INS task of NIST TRECVID 2021, CAAF uses 25% fewer feedback samples to achieve a performance that is nearly equivalent to the champion solution. Moreover, with the same number of feedback samples, CAAF's mAP is 51.9%, significantly surpassing the champion solution by 5.9%. Code is available athttps://github.com/nercms-mmap/caaf.