Channel Attention was first used as a squeeze and excitation block for classification, which generates channel attention maps by using the relationship between the channels.

• Squeeze-and-Excitation Networks(CVPR) [paper][code]

• Image Super-Resolution Using Very Deep Residual Channel Attention Networks(ECCV).[paper][code]
• CBAM: Convolutional Block Attention Module(ECCV).[paper][keras][code]
• BAM: Bottleneck Attention Module(BMVC).[paper][code]
• (Learning a Discriminative Feature Network for Semantic Segmentation).[paper]

• RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting(MICCAI).[paper]
• Bilinear Attention Networks for Person Retrieval(ICCV).[paper][code]
• DenseNet with Deep Residual Channel-Attention Blocks for Single Image Super Resolution(CVPR Workshop).[paper][code]

• ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks(CVPR).[paper][code]
• ResNeSt: Split-Attention Networks.[paper][code]
• Channel Attention Residual U-Net for Retinal Vessel Segmentation.[paper][code]