论文标题
零件感知的原型网络，用于几个射击语义分段
Part-aware Prototype Network for Few-shot Semantic Segmentation
论文作者
论文摘要
很少有语义细分旨在学习仅使用几个带注释的示例来细分新对象类，这些示例具有广泛的现实应用程序。大多数现有的方法要么集中于单向少细分的限制性设置，要么遭受对象区域的不完整覆盖范围。在本文中，我们提出了一个基于原型表示的新颖的几声语义分割框架。我们的关键思想是将整体阶级表示分解为一组部分感知的原型，能够捕获多样化和细粒的对象特征。此外，我们建议利用未标记的数据来丰富我们的零件感知原型，从而更好地对语义对象的类内部变化进行更好的建模。我们开发了一种新型的图形神经网络模型，以基于标记和未标记的图像生成和增强所提出的部分感知原型。对两个基准测试的广泛实验评估表明，我们的方法的表现优于先前的艺术，并具有相当大的边距。
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.
