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。

语言引导的少样本语义分割

Language-guided Few-shot Semantic Segmentation

Knowledge distillation is a method of transferring the knowledge from a
complex deep neural network (DNN) to a smaller and faster DNN, while preserving
its accuracy. Recent variants of knowledge distillation include teaching
assistant distillation, curriculum distillation, mask distillation, and
decoupling distillation, which aim to improve the performance of knowledge
distillation by introducing additional components or by changing the learning
process. Teaching assistant distillation involves an intermediate model called
the teaching assistant, while curriculum distillation follows a curriculum
similar to human education. Mask distillation focuses on transferring the
attention mechanism learned by the teacher, and decoupling distillation
decouples the distillation loss from the task loss. Overall, these variants of
knowledge distillation have shown promising results in improving the
performance of knowledge distillation.

知识蒸馏是一种将深度神经网络的知识转移到更小更快的神经网络中的方法，近期变体包括教学助理蒸馏、课程蒸馏、遮罩蒸馏和解耦蒸馏等，致力于通过引入额外的组件或改变学习过程来提高知识蒸馏的性能。