CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning

TL;DR

CANet introduces a class-agnostic segmentation network capable of few-shot learning, utilizing multi-level feature comparison, iterative refinement, and attention mechanisms to outperform existing methods on PASCAL VOC 2012.

Contribution

The paper presents a novel class-agnostic segmentation network with an iterative refinement process and attention-based support fusion for few-shot segmentation tasks.

Findings

Achieves 55.4% mIoU for 1-shot segmentation on PASCAL VOC 2012.

Outperforms state-of-the-art methods by 14.6% in 1-shot setting.

Effective multi-level feature comparison and attention mechanisms improve segmentation accuracy.

Abstract

Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.