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.

翻译：少样本分割旨在通过仅使用少量基类支撑图像，对查询图像中的新类别进行分割。然而，在跨域少样本分割任务中，由于域差异的存在，利用标签丰富域的特征来服务于资源受限域面临诸多挑战。本文提出一种动态自适应精炼方法，旨在平衡跨域少样本分割中的泛化性与特异性。该方法包含通道统计量扰动策略，通过扰动源域中的特征通道统计量来增强对未知目标域的泛化能力。此外，针对不同目标域的差异性，我们提出自适应精炼自匹配方法，该方法可动态调整匹配阈值，并利用自匹配机制对预测结果进行精炼，从而提升分割精度。同时，我们还提出测试时自适应方法，以增强模型对不同特征分布的适应性。实验结果表明，所提方法在跨域少样本分割任务中取得了优于现有先进方法的性能。