  Few-shot segmentation (FSS) aims to segment novel classes in a query image by
using only a small number of supporting images from base classes. However, in
cross-domain few-shot segmentation (CD-FSS), leveraging features from
label-rich domains for resource-constrained domains poses challenges due to
domain discrepancies. This work presents a Dynamically Adaptive Refine (DARNet)
method that aims to balance generalization and specificity for CD-FSS. Our
method includes the Channel Statistics Disruption (CSD) strategy, which
perturbs feature channel statistics in the source domain, bolstering
generalization to unknown target domains. Moreover, recognizing the variability
across target domains, an Adaptive Refine Self-Matching (ARSM) method is also
proposed to adjust the matching threshold and dynamically refine the prediction
result with the self-matching method, enhancing accuracy. We also present a
Test-Time Adaptation (TTA) method to refine the model's adaptability to diverse
feature distributions. Our approach demonstrates superior performance against
state-of-the-art methods in CD-FSS tasks.
