Few-shot segmentation (FSS) aims to train a model which can segment the object from novel classes with a few labeled samples. The insufficient generalization ability of models leads to unsatisfactory performance when the models lack enough labeled data from the novel classes. Considering that there are abundant unlabeled data available, it is promising to improve the generalization ability by exploiting these various data. For leveraging unlabeled data, we propose a novel method, named Image to Pseudo-Episode (IPE), to generate pseudo-episodes from unlabeled data. Specifically, our method contains two modules, i.e., the pseudo-label generation module and the episode generation module. The former module generates pseudo-labels from unlabeled images by the spectral clustering algorithm, and the latter module generates pseudo-episodes from pseudo-labeled images by data augmentation methods. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ demonstrate that our method achieves the state-of-the-art performance for FSS.

翻译：小样本分割（FSS）旨在训练一个能够使用少量标注样本从新类别中分割出目标的模型。当模型缺乏来自新类别的充足标注数据时，其泛化能力不足导致性能不理想。考虑到存在大量无标注数据可用，通过利用这些多样化数据提升泛化能力是可行的。为了利用无标注数据，我们提出了一种名为图像到伪片段（IPE）的新方法，从无标注数据中生成伪片段。具体而言，我们的方法包含两个模块，即伪标签生成模块和片段生成模块。前者通过谱聚类算法从无标注图像生成伪标签，后者通过数据增强方法从伪标注图像生成伪片段。在PASCAL-$5^i$和COCO-$20^i$上的广泛实验表明，我们的方法实现了FSS的最优性能。