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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Arnet: Attention-Based Refinement Network for Few-Shot Semantic Segmentation
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Arnet: Attention-Based Refinement Network for Few-Shot Semantic Segmentation

机译：Arnet：基于注意力的细化语义分割网络

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摘要

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.
机译：语义分割对于计算机视觉来说是一项具有挑战性的任务，旨在从像素级别对对象进行分类。以前基于深度学习的方法已经取得了一些进展，但是标记工作非常耗时。很少有语义分割可以缓解此问题。本文提出了一种基于注意力的细化语义分割细化网络（ARNet），它由三个分支组成：引导分支，分割分支和细化分支。剩余注意力模块（RAM）可以突出显示细分分支中的功能，从而为精炼分支提供更好的指导。细化分支末尾的并行膨胀卷积模块（PDCM）可以细化分割结果。在PASCAL VOC 2012数据集上进行的实验表明，我们的模型在单次分割中的平均交叉点合并（mIoU）得分为48.1％，在五次分割中的平均交叉点得分为49.1％，比最新方法高1.8 ％和2.0％。

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