论文标题
测试时间有效的UCDR的测试时间培训
Test-time Training for Data-efficient UCDR
论文作者
论文摘要
在广义测试场景下的图像检索已经在文献中获得了显着的动力，而最近提出的通用跨域检索方案是该方向的先驱。任何此类广义分类或检索算法的常见实践是在训练过程中利用许多域中的样本来学习数据的域不变表示。这种标准通常是限制性的，因此在这项工作中，我们首次以数据有效的方式探索了广义检索问题。具体而言，我们旨在通过将模型调整在利用自我监督的学习技术的测试数据上，将任何预训练的跨域检索网络推广到任何未知的查询域/类别。为了实现这一目标，我们探索了不同的自我监督损失功能〜（例如，rotnet，jigsaw，barlow twins等），并分析了它们的有效性。广泛的实验表明，所提出的方法简单，易于实现，并且可以有效地处理有效的UCDR。
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.
