Recent work has made significant progress in improving spatial resolution for
pixelwise labeling with Fully Convolutional Network (FCN) framework by
employing Dilated/Atrous convolution, utilizing multi-scale features and
refining boundaries. In this paper, we explore the impact of global contextual
information in semantic segmentation by introducing the Context Encoding
Module, which captures the semantic context of scenes and selectively
highlights class-dependent featuremaps. The proposed Context Encoding Module
significantly improves semantic segmentation results with only marginal extra
computation cost over FCN. Our approach has achieved new state-of-the-art
results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single
model achieves a final score of 0.5567 on ADE20K test set, which surpass the
winning entry of COCO-Place Challenge in 2017. In addition, we also explore how
the Context Encoding Module can improve the feature representation of
relatively shallow networks for the image classification on CIFAR-10 dataset.
Our 14 layer network has achieved an error rate of 3.45%, which is comparable
with state-of-the-art approaches with over 10 times more layers. The source
code for the complete system are publicly available.

本文提出了一种 Context Encoding Module 方法，在原有 FCN 框架的基础上加入了全局场景语义信息来优化语义分割结果，在 PASCAL-Context、PASCAL VOC 2012 和 ADE20K 数据集上取得了最新最优的成果，并且通过在 CIFAR-10 数据集上进行对比实验表明该方法可以用来改善相对浅层网络的特征表示效果。