Image retrieval is a niche problem in computer vision curated towards finding similar images in a database using a query. In this work, for the first time in literature, we employ test-time training techniques for adapting to distribution shifts under Universal Cross-Domain Retrieval (UCDR). Test-time training has previously been shown to reduce generalization error for image classification, domain adaptation, semantic segmentation, and zero-shot sketch-based image retrieval (ZS-SBIR). In UCDR, in addition to the semantic shift of unknown categories present in ZS-SBIR, the presence of unknown domains leads to even higher distribution shifts. To bridge this domain gap, we use self-supervision through 3 different losses - Barlow Twins, Jigsaw Puzzle and RotNet on a pretrained network at test-time. This simple approach leads to improvements on UCDR benchmarks and also improves model robustness under a challenging cross-dataset generalization setting.

翻译：计算机图像检索是计算机视野中的一个特殊问题,在利用查询在数据库中查找类似的图像。在这项工作中,我们首次在文献中采用了测试时间培训技术,以适应环球跨域检索(UCDR)下分布变化。测试时间培训以前已经显示可以减少图像分类、域名适应、语义分解和零光素描图图像检索(ZS-SBIR)方面的一般错误。在UCDR中,除了ZS-SBIR中存在的未知类别的语义转换外,未知域的存在还导致更大的分布变化。为了弥合这一域间差距,我们在测试时在预先训练的网络上使用自我监督的三种不同的损失—— Barlow Twins、Jigsaw Puzzlet和RotNet。这一简单的方法可以改进UCDR基准,并在具有挑战性的交叉数据集通用设置下改进模型的稳健性。