Despite impressive progress in object detection over the last years, it is
still an open challenge to reliably detect objects across visual domains.
Although the topic has attracted attention recently, current approaches all
rely on the ability to access a sizable amount of target data for use at
training time. This is a heavy assumption, as often it is not possible to
anticipate the domain where a detector will be used, nor to access it in
advance for data acquisition. Consider for instance the task of monitoring
image feeds from social media: as every image is created and uploaded by a
different user it belongs to a different target domain that is impossible to
foresee during training. This paper addresses this setting, presenting an
object detection algorithm able to perform unsupervised adaption across domains
by using only one target sample, seen at test time. We achieve this by
introducing a multi-task architecture that one-shot adapts to any incoming
sample by iteratively solving a self-supervised task on it. We further enhance
this auxiliary adaptation with cross-task pseudo-labeling. A thorough benchmark
analysis against the most recent cross-domain detection methods and a detailed
ablation study show the advantage of our method, which sets the
state-of-the-art in the defined one-shot scenario.

本篇论文介绍一种多任务架构的目标检测算法，利用一次性适应的方法，自动适应不同的域，无需访问大量的目标数据，其中利用自监督任务和伪标签方法加强了适应，对比最新的跨域检测方法，研究结果显示该方法有优势，是 one-shot 情况下的最佳方法。