Few-shot semantic segmentation (FSS) aims to form class-agnostic models
segmenting unseen classes with only a handful of annotations. Previous methods
limited to the semantic feature and prototype representation suffer from coarse
segmentation granularity and train-set overfitting. In this work, we design
Hierarchically Decoupled Matching Network (HDMNet) mining pixel-level support
correlation based on the transformer architecture. The self-attention modules
are used to assist in establishing hierarchical dense features, as a means to
accomplish the cascade matching between query and support features. Moreover,
we propose a matching module to reduce train-set overfitting and introduce
correlation distillation leveraging semantic correspondence from coarse
resolution to boost fine-grained segmentation. Our method performs decently in
experiments. We achieve $50.0\%$ mIoU on \coco~dataset one-shot setting and
$56.0\%$ on five-shot segmentation, respectively.

本文提出了一种基于 transformer 架构的 Hierarchically Decoupled Matching Network（HDMNet）用于 few-shot 语义分割任务。该网络利用自注意力机制来建立层次化的密集特征并使用匹配模块降低过拟合，引入来自粗分辨率的语义对应的相关性传递，以提高细粒度的分割。在实验中该方法表现良好，在 COCO 数据集上的 1-shot 和 5-shot 分割分别达到了 50.0% 和 56.0% 的 mIoU。