In the domain adaptation problem, source data may be unavailable to the
target client side due to privacy or intellectual property issues. Source-free
unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the
source side to align the target distribution with only the source model and
unlabeled target data. The source model usually produces noisy and
context-inconsistent pseudo-labels on the target domain, i.e., neighbouring
regions that have a similar visual appearance are annotated with different
pseudo-labels. This observation motivates us to refine pseudo-labels with
context relations. Another observation is that features of the same class tend
to form a cluster despite the domain gap, which implies context relations can
be readily calculated from feature distances. To this end, we propose a
context-aware pseudo-label refinement method for SF-UDA. Specifically, a
context-similarity learning module is developed to learn context relations.
Next, pseudo-label revision is designed utilizing the learned context
relations. Further, we propose calibrating the revised pseudo-labels to
compensate for wrong revision caused by inaccurate context relations.
Additionally, we adopt a pixel-level and class-level denoising scheme to select
reliable pseudo-labels for domain adaptation. Experiments on cross-domain
fundus images indicate that our approach yields the state-of-the-art results.
Code is available at https://github.com/xmed-lab/CPR.Comment: Accepted by MICCAI 2023, 11 page