  In this paper we present a solution to the task of "unsupervised domain
adaptation (UDA) of a given pre-trained semantic segmentation model without
relying on any source domain representations". Previous UDA approaches for
semantic segmentation either employed simultaneous training of the model in the
source and target domains, or they relied on an additional network, replaying
source domain knowledge to the model during adaptation. In contrast, we present
our novel Unsupervised BatchNorm Adaptation (UBNA) method, which adapts a given
pre-trained model to an unseen target domain without using -- beyond the
existing model parameters from pre-training -- any source domain
representations (neither data, nor networks) and which can also be applied in
an online setting or using just a few unlabeled images from the target domain
in a few-shot manner. Specifically, we partially adapt the normalization layer
statistics to the target domain using an exponentially decaying momentum
factor, thereby mixing the statistics from both domains. By evaluation on
standard UDA benchmarks for semantic segmentation we show that this is superior
to a model without adaptation and to baseline approaches using statistics from
the target domain only. Compared to standard UDA approaches we report a
trade-off between performance and usage of source domain representations.
