Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples and has made great progress recently. Most of the existing FSS models focus on the feature matching between support and query to tackle FSS. However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction. To this end, we propose a Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate latent context structure in query images. Specifically, we propose a Support-induced Graph Reasoning (SiGR) module to capture salient query object parts at different semantic levels with a Support-induced GCN. Furthermore, an instance association (IA) module is designed to capture high-order instance context from both support and query instances. By integrating the proposed two modules, SiGCN can learn rich query context representation, and thus being more robust to appearance variations. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our SiGCN achieves state-of-the-art performance.

翻译：少样本语义分割旨在仅利用少量标注样本实现新类别物体的分割，近年来取得了显著进展。现有大多数少样本语义分割模型侧重于支持图像与查询图像之间的特征匹配。然而，同一类别物体间的外观差异可能极大，导致不可靠的特征匹配与查询掩码预测。为此，我们提出基于支持图卷积网络，以显式挖掘查询图像中的潜在上下文结构。具体而言，我们设计了支持诱导图推理模块，通过支持图卷积网络捕获不同语义层次下的显著查询物体部件。此外，设计了实例关联模块，从支持图像与查询图像中同时捕获高阶实例上下文。通过整合上述两个模块，基于支持图卷积网络可学习丰富的查询上下文表示，从而对外观差异具有更强的鲁棒性。在PASCAL-5i与COCO-20i数据集上的大量实验表明，我们的基于支持图卷积网络达到了最先进的性能。