Image retrieval under generalized test scenarios has gained significant momentum in literature, and the recently proposed protocol of Universal Cross-domain Retrieval is a pioneer in this direction. A common practice in any such generalized classification or retrieval algorithm is to exploit samples from many domains during training to learn a domain-invariant representation of data. Such criterion is often restrictive, and thus in this work, for the first time, we explore the generalized retrieval problem in a data-efficient manner. Specifically, we aim to generalize any pre-trained cross-domain retrieval network towards any unknown query domain/category, by means of adapting the model on the test data leveraging self-supervised learning techniques. Toward that goal, we explored different self-supervised loss functions~(for example, RotNet, JigSaw, Barlow Twins, etc.) and analyze their effectiveness for the same. Extensive experiments demonstrate the proposed approach is simple, easy to implement, and effective in handling data-efficient UCDR.

翻译：图像检索在广义测试场景下的研究已在文献中获得显著进展，近期提出的通用跨域检索协议是该方向的开拓性工作。此类广义分类或检索算法的常见做法是在训练阶段利用多领域样本学习数据的域不变表征。这种准则往往具有局限性，因此本研究首次以数据高效方式探索广义检索问题。具体而言，我们旨在通过利用自监督学习技术对模型进行测试时自适应，将任意预训练的跨域检索网络泛化到未知的查询域/类别。为此目标，我们探索了不同自监督损失函数（例如RotNet、JigSaw、Barlow Twins等），并分析了它们在数据高效UCDR中的有效性。大量实验表明，所提方法在应对数据高效UCDR问题时具有简单易实现且高效的特点。