  Deep detection approaches are powerful in controlled conditions, but appear
brittle and fail when source models are used off-the-shelf on unseen domains.
Most of the existing works on domain adaptation simplify the setting and access
jointly both a large source dataset and a sizable amount of target samples.
However this scenario is unrealistic in many practical cases as when monitoring
image feeds from social media: only a pretrained source model is available and
every target image uploaded by the users belongs to a different domain not
foreseen during training. We address this challenging setting by presenting an
object detection algorithm able to exploit a pre-trained source model and
perform unsupervised adaptation by using only one target sample seen at test
time. Our multi-task architecture includes a self-supervised branch that we
exploit to meta-train the whole model with single-sample cross-domain episodes,
and prepare to the test condition. At deployment time the self-supervised task
is iteratively solved on any incoming sample to one-shot adapt on it. We
introduce a new dataset of social media image feeds and present a thorough
benchmark with the most recent cross-domain detection methods showing the
advantages of our approach.
