1.BAM
BAM: Bottleneck Attention Module，BMVC 2018
对SENet的改进：1）channel attention和SENet一样；2）SENet只关注channel attention，BAM增加了spatial attention，说白了过几个conv，其中有两个dilated conv去学spatial context information
2.CBAM
CBAM: Convolutional Block Attention Module，ECCV 2018
对SENet的改进：1）SENet采用global average pooling来embed spatial global information，CBAM同时使用了avg/max pooling；2）SENet只关注channel attention，CBAM增加了spatial attention，也是采用avg/max pooling去embed channel global information
无论是SENet，BAM，CBAM，无论是channel/spatial attention branch，相应的branch总得做点什么（比如pooling，dilated conv）去学该branch的context information，而不能简单地过conv和sigmoid ？？
3.Non-Local 系列
“capture long-range dependencies directly by computing interactions between ay two positions, regardless of their positional distance”
Non-local Neural Networks，CVPR 2018
A^2-Nets: Double Attention Networks，NIPS 2018
两篇用Non-Local做分割的，感觉没啥区别：
Dual Attention Network for Scene Segmentation，AAAI 2019
OCNet: Object Context Network for Scene Parsing，arxiv 2018
最先有Non-Local思想应该是Attention Is All You Need：
Attention Is All You Need，NIPS 2017
4.PAN
Pyramid Attention Network for Semantic Segmentation，BMVC 2018
“Furthermore, high-level features with abundant category information can be used to weight low-level information to select precise resolution details.”
“Our Global Attention Upsample module performs global average pooling to provide global context as a guidance of low-level features to select category localization details.”