Recent few-shot segmentation (FSS) methods introduce an extra pre-training stage before meta-training to obtain a stronger backbone, which has become a standard step in few-shot learning. Despite the effectiveness, current pre-training scheme suffers from the merged background problem: only base classes are labelled as foregrounds, making it hard to distinguish between novel classes and actual background. In this paper, we propose a new pre-training scheme for FSS via decoupling the novel classes from background, called Background Clustering Pre-Training (BCPT). Specifically, we adopt online clustering to the pixel embeddings of merged background to explore the underlying semantic structures, bridging the gap between pre-training and adaptation to novel classes. Given the clustering results, we further propose the background mining loss and leverage base classes to guide the clustering process, improving the quality and stability of clustering results. Experiments on PASCAL-5i and COCO-20i show that BCPT yields advanced performance. Code will be available.

翻译：近期少样本分割方法在元训练前引入了额外的预训练阶段，以获取更强的骨干网络，这已成为少样本学习中的标准步骤。尽管有效，当前预训练方案存在背景合并问题：仅将基类标注为前景，导致难以区分新类与真实背景。本文提出一种通过从背景中解耦新类的预训练方案，称为背景聚类预训练。具体而言，我们对合并背景的像素嵌入采用在线聚类，以探究其潜在语义结构，从而弥合预训练与新类适应之间的差距。基于聚类结果，我们进一步提出背景挖掘损失，并利用基类引导聚类过程，提升聚类结果的质量与稳定性。在PASCAL-5i和COCO-20i上的实验表明，BCPT取得了先进性能。代码将公开。