Self-Support Prototype-Aware For Few-Shot Semantic Segmentation

In recent years, significant progress has been made in prototype-based learning methods for few-shot semantic segmentation. However, prototype features originating from the support images are interfered with by intra-class diversity and thus cannot be aligned with the query foreground, resulting in poor segmentation accuracy. Therefore, we propose a novel self-support prototype-aware (SSPA) network to obtain highly confident query foreground pixel points and their corresponding query features. We design Cycle Consistency Collection module and Self-Support Collection module to address the interference of invalid support prototypes. Experimental results demonstrate that our SSPA significantly improves the quality of prototypes and achieves state-of-the-art segmentation results on multiple datasets. In particular, SSPA achieves mIoU scores of 69.7% and 76.4% for 1-shot and 5-shot segmentation, respectively, on PASCAL-5ⁱ.

I. Introduction

Achieving high performance in semantic segmentation relies heavily on pixel-level annotations [1], [2], which can be expensive to obtain in large quantities. Therefore, there is a need to explore methods to achieve good performance with a limited amount of annotated data. Few-shot semantic segmentation has emerged as a solution to this problem [3]–[5]. The goal of few-shot semantic segmentation is to segment unseen classes using only a few support samples and to learn transferable knowledge during the training process.