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。

语义分割的压缩注意力网络

Squeeze-and-Attention Networks for Semantic Segmentation

Salient segmentation aims to segment out attention-grabbing regions, a
critical yet challenging task and the foundation of many high-level computer
vision applications. It requires semantic-aware grouping of pixels into salient
regions and benefits from the utilization of global multi-scale contexts to
achieve good local reasoning. Previous works often address it as two-class
segmentation problems utilizing complicated multi-step procedures including
refinement networks and complex graphical models. We argue that semantic
salient segmentation can instead be effectively resolved by reformulating it as
a simple yet intuitive pixel-pair based connectivity prediction task. Following
the intuition that salient objects can be naturally grouped via semantic-aware
connectivity between neighboring pixels, we propose a pure Connectivity Net
(ConnNet). ConnNet predicts connectivity probabilities of each pixel with its
neighboring pixels by leveraging multi-level cascade contexts embedded in the
image and long-range pixel relations. We investigate our approach on two tasks,
namely salient object segmentation and salient instance-level segmentation, and
illustrate that consistent improvements can be obtained by modeling these tasks
as connectivity instead of binary segmentation tasks for a variety of network
architectures. We achieve state-of-the-art performance, outperforming or being
comparable to existing approaches while reducing inference time due to our less
complex approach.

该论文将显著分割重新定义为基于像素对连接性预测的任务，并提出了一种名为 ConnNet 的新方法，该方法通过利用图像中嵌入的多级级联语境和长距离像素关系预测每个像素与其相邻像素的连接概率，成功地实现了显著分割和显著实例级分割。该方法比现有方法更少复杂，具有更好的性能。