Source-Free Open Compound Domain Adaptation in Semantic Segmentation

In this work, we introduce a new concept, named
source-free open compound domain adaptation (SF-OCDA), and
study it in semantic segmentation. SF-OCDA is more challenging
than the traditional domain adaptation but it is more practical.
It jointly considers (1) the issues of data privacy and data
storage and (2) the scenario of multiple target domains and
unseen open domains. In SF-OCDA, only the source pre-trained
model and the target data are available to learn the target
model. The model is evaluated on the samples from the target
and unseen open domains. To solve this problem, we present
an effective framework by separating the training process into
two stages: (1) pre-training a generalized source model and (2)
adapting a target model with self-supervised learning. In our
framework, we propose the Cross-Patch Style Swap (CPSS) to
diversify samples with various patch styles in the feature-level,
which can benefit the training of both stages. First, CPSS can
significantly improve the generalization ability of the source
model, providing more accurate pseudo-labels for the latter stage.
Second, CPSS can reduce the influence of noisy pseudo-labels and
also avoid the model overfitting to the target domain during selfsupervised
learning, consistently boosting the performance on
the target and open domains. Experiments demonstrate that our
method produces state-of-the-art results on the C-Driving dataset.
Furthermore, our model also achieves the leading performance
on CityScapes for domain generalization.