The recent integration of attention mechanisms into segmentation networks
improves their representational capabilities through a great emphasis on more
informative features. However, these attention mechanisms ignore an implicit
sub-task of semantic segmentation and are constrained by the grid structure of
convolution kernels. In this paper, we propose a novel squeeze-and-attention
network (SANet) architecture that leverages an effective squeeze-and-attention
(SA) module to account for two distinctive characteristics of segmentation: i)
pixel-group attention, and ii) pixel-wise prediction. Specifically, the
proposed SA modules impose pixel-group attention on conventional convolution by
introducing an 'attention' convolutional channel, thus taking into account
spatial-channel inter-dependencies in an efficient manner. The final
segmentation results are produced by merging outputs from four hierarchical
stages of a SANet to integrate multi-scale contexts for obtaining an enhanced
pixel-wise prediction. Empirical experiments on two challenging public datasets
validate the effectiveness of the proposed SANets, which achieves 83.2% mIoU
(without COCO pre-training) on PASCAL VOC and a state-of-the-art mIoU of 54.4%
on PASCAL Context.

本文提出了一种新颖的挤压和注意力（SA）模块，结合传统卷积实现像素组关注和像素级预测，最终通过融合四个层次的 SANet 的输出来集成多尺度的上下文信息，从而实现了在 PASCAL VOC 和 PASCAL 上的语义分割任务中表现出色。