Pyramid Convolutional Attention Network is proposed to efficiently capture long-range dependency and fuse features from different levels for benefitting semantic segmentation problems. In this paper, we focus on how to extract more representative features for segmentation object recognition and design a decoder to recover details in a more efficient way. Inspired by atrous sampling and attention mechanism, we propose Pyramid Atrous Attention module to capture long-range dependency for learning richer contextual features. We also find that features of different levels have diverse representation so we design Convolutional Attention Refinement module to provide global context for low-level features and local details for high-level features. By combining with these two efficient module, we construct our Pyramid Convolutional Attention Network (PCANet), which achieves state-of-the-art results on Pascal VOC 2012 and Cityscapes benchmark.

提出了金字塔卷积注意网络，以有效地捕获远程依存关系，并融合不同级别的特征，从而有益于语义分割问题。在本文中，我们专注于如何为分割对象识别提取更多具有代表性的特征，以及如何设计解码器以更有效地恢复细节。受不规则采样和注意机制的启发，我们提出了金字塔不规则注意模块，以捕获远程依赖关系，以学习更丰富的上下文特征。我们还发现不同级别的要素具有不同的表示形式，因此我们设计了卷积注意力细化模块，以提供低级别要素的全局上下文和高级别要素的局部细节。通过结合这两个有效的模块，