Part-aware Prototype Network for Few-shot Semantic Segmentation

Understand

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.