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显著下降，有效限制了实际应用场景。为此，近期出现了跨域小样本分割（CD-FSS）的研究。该任务的主要方法尝试在源域上以跨域泛化的方式学习分割。令人惊讶的是，我们可以在消除训练阶段并移除其主要分割网络的情况下超越这些方法。我们发现测试时任务适应才是CD-FSS成功的关键。通过在常规分类预训练骨干网络的特征金字塔上附加小型网络实现任务适应。为避免监督微调中过拟合少量标注样本，在学习附加层参数时，以输入图像增强视图间的一致性作为指导。尽管我们自限于测试时仅使用少量标注样本，仍取得了CD-FSS的最新最佳性能，这证明了重新思考该任务方法的必要性。