  Unsupervised Domain Adaptation (UDA) can tackle the challenge that
convolutional neural network(CNN)-based approaches for semantic segmentation
heavily rely on the pixel-level annotated data, which is labor-intensive.
However, existing UDA approaches in this regard inevitably require the full
access to source datasets to reduce the gap between the source and target
domains during model adaptation, which are impractical in the real scenarios
where the source datasets are private, and thus cannot be released along with
the well-trained source models. To cope with this issue, we propose a
source-free domain adaptation framework for semantic segmentation, namely SFDA,
in which only a well-trained source model and an unlabeled target domain
dataset are available for adaptation. SFDA not only enables to recover and
preserve the source domain knowledge from the source model via knowledge
transfer during model adaptation, but also distills valuable information from
the target domain for self-supervised learning. The pixel- and patch-level
optimization objectives tailored for semantic segmentation are seamlessly
integrated in the framework. The extensive experimental results on numerous
benchmark datasets highlight the effectiveness of our framework against the
existing UDA approaches relying on source data.
