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.

本研究提出了一种新的针对少样本分割的预训练方案，通过将新颖类别与背景解耦来解决合并背景问题，称为背景聚类预训练（BCPT），实验证明 BCPT 在 PASCAL-5i 和 COCO-20i 数据集上获得了先进的性能表现。