Few-Shot Segmentation via Rich Prototype Generation and Recurrent Prediction Enhancement

TL;DR

This paper introduces a novel few-shot segmentation method that employs rich prototype generation and recurrent prediction enhancement to better handle scale variations and improve feature integration, leading to superior performance.

Contribution

The paper proposes RPGM and RPEM modules that enhance prototype diversity and feature propagation, advancing few-shot segmentation techniques.

Findings

Outperforms existing methods on PASCAL-5i and COCO-20i benchmarks.

Effectively handles objects with various scales.

Improves feature integration through recurrent decoding.

Abstract

Prototype learning and decoder construction are the keys for few-shot segmentation. However, existing methods use only a single prototype generation mode, which can not cope with the intractable problem of objects with various scales. Moreover, the one-way forward propagation adopted by previous methods may cause information dilution from registered features during the decoding process. In this research, we propose a rich prototype generation module (RPGM) and a recurrent prediction enhancement module (RPEM) to reinforce the prototype learning paradigm and build a unified memory-augmented decoder for few-shot segmentation, respectively. Specifically, the RPGM combines superpixel and K-means clustering to generate rich prototype features with complementary scale relationships and adapt the scale gap between support and query images. The RPEM utilizes the recurrent mechanism to design a round-way propagation decoder. In this way, registered features can provide object-aware information continuously. Experiments show that our method consistently outperforms other competitors on two popular benchmarks PASCAL-${{5}^{i}}$ and COCO-${{20}^{i}}$.