Few-shot semantic segmentation aims to learn to segment new object classes with only a few annotated examples, which has a wide range of real-world applications. Most existing methods either focus on the restrictive setting of one-way few-shot segmentation or suffer from incomplete coverage of object regions. In this paper, we propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes, capable of capturing diverse and fine-grained object features. In addition, we propose to leverage unlabeled data to enrich our part-aware prototypes, resulting in better modeling of intra-class variations of semantic objects. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes based on labeled and unlabeled images. Extensive experimental evaluations on two benchmarks show that our method outperforms the prior art with a sizable margin.
翻译:少见的语义分解法旨在学习分解新对象类别,只有几个附加说明的例子,这些例子具有广泛的现实世界应用。大多数现有方法要么侧重于单向微粒分解的限制性设置,要么对目标区域进行不完整的覆盖。在本文中,我们提议了一个基于原型表示法的新颖的几发语义分解框架。我们的关键想法是将整体分类代表制分解成一套能捕捉多样化和细微刻度天体特征的分辨原型。此外,我们提议利用未贴标签的数据来丰富我们的单向微粒原型,从而更好地模拟类内变异语义物体的模型。我们开发了一个新型的图形神经网络模型,以生成和加强基于标签和无标签图像的拟议半透明原型。对两个基准进行的广泛实验评估表明,我们的方法超越了前一种艺术,具有相当大的空间。