Few-shot segmentation performance declines substantially when facing images from a domain different than the training domain, effectively limiting real-world use cases. To alleviate this, recently cross-domain few-shot segmentation (CD-FSS) has emerged. Works that address this task mainly attempted to learn segmentation on a source domain in a manner that generalizes across domains. Surprisingly, we can outperform these approaches while eliminating the training stage and removing their main segmentation network. We show test-time task-adaption is the key for successful CD-FSS instead. Task-adaption is achieved by appending small networks to the feature pyramid of a conventionally classification-pretrained backbone. To avoid overfitting to the few labeled samples in supervised fine-tuning, consistency across augmented views of input images serves as guidance while learning the parameters of the attached layers. Despite our self-restriction not to use any images other than the few labeled samples at test time, we achieve new state-of-the-art performance in CD-FSS, evidencing the need to rethink approaches for the task.

翻译：小样本分割在面对与训练域不同的图像时，性能会显著下降，从而限制了实际应用。为解决此问题，跨域小样本分割（CD-FSS）近期应运而生。针对该任务的研究主要尝试在源域上学习一种能够跨域泛化的分割方法。令人意外的是，我们不仅能超越这些方法，还能完全消除训练阶段并移除其主分割网络。实验表明，测试时任务适应才是实现CD-FSS成功的关键。具体而言，通过在常规分类预训练主干网络的特征金字塔上附加小型网络，即可实现任务适应。为避免监督微调时对少量标注样本的过拟合，我们采用输入图像增强视图间的一致性作为引导，来学习附加网络层的参数。尽管我们严格要求在测试阶段仅使用少量标注样本而不借助其他图像，仍取得了CD-FSS任务的最新最优性能，这充分证明需要重新审视该类任务的技术路线。