Arnet: Attention-Based Refinement Network for Few-Shot Semantic Segmentation

Semantic segmentation is a challenging task for computer vision which aims to classify the objects from the pixel level. Previous methods based on deep learning have made some progress but the labeling work is very time-consuming. Few-shot semantic segmentation can alleviate this problem. In this paper, we propose an Attention-based Refinement Network (ARNet) for few-shot semantic segmentation, which consists of three branches: the guidance branch, the segmentation branch and the refinement branch. The Residual Attention Module (RAM) can highlight the features from segmentation branch, giving a better guidance to refinement brach. And the Parallel Dilated Convolution Module (PDCM) in the end of refinement branch can refine the segmentation results. Experiments on PASCAL VOC 2012 dataset show that our model achieves a mean Intersection-over-Union (mIoU) score of 48.1% for one-shot segmentation and 49.1% for five-shot segmentation, outperforming state-of-the-art methods by 1.8% and 2.0%, respectively.