Few-shot learning is a promising way for reducing the label cost in new
categories adaptation with the guidance of a small, well labeled support set.
But for few-shot semantic segmentation, the pixel-level annotations of support
images are still expensive. In this paper, we propose an innovative solution to
tackle the challenge of few-shot semantic segmentation using only language
information, i.e.image-level text labels. Our approach involves a
vision-language-driven mask distillation scheme, which contains a
vision-language pretraining (VLP) model and a mask refiner, to generate high
quality pseudo-semantic masks from text prompts. We additionally introduce a
distributed prototype supervision method and complementary correlation matching
module to guide the model in digging precise semantic relations among support
and query images. The experiments on two benchmark datasets demonstrate that
our method establishes a new baseline for language-guided few-shot semantic
segmentation and achieves competitive results to recent vision-guided methods.

通过语言信息进行语言指导的少样本语义分割，使用视觉 - 语言预训练模型和遮罩优化来生成高质量伪语义遮罩，引入分布式原型监督方法和互补相关匹配模块来指导模型挖掘支持和查询图像的精确语义关系。在两个基准数据集上的实验表明，我们的方法为语言指导的少样本语义分割建立了新的基准，并达到了与最近的视觉指导方法竞争的结果。