SENet pioneered channel attention. The core of SENet is a squeeze-and-excitation (SE) block which is used to collect global information, capture channel-wise relationships and improve representation ability. SE blocks are divided into two parts, a squeeze module and an excitation module. Global spatial information is collected in the squeeze module by global average pooling. The excitation module captures channel-wise relationships and outputs an attention vector by using fully-connected layers and non-linear layers (ReLU and sigmoid). Then, each channel of the input feature is scaled by multiplying the corresponding element in the attention vector. Overall, a squeeze-and-excitation block $F_\text{se}$ (with parameter $\theta$) which takes $X$ as input and outputs $Y$ can be formulated as: \begin{align} s = F_\text{se}(X, \theta) & = \sigma (W_{2} \delta (W_{1}\text{GAP}(X))) \end{align} \begin{align} Y = sX \end{align}
Source: Squeeze-and-Excitation NetworksPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Semantic Segmentation | 30 | 5.32% |
Super-Resolution | 22 | 3.90% |
Image Super-Resolution | 21 | 3.72% |
Decoder | 21 | 3.72% |
Object Detection | 17 | 3.01% |
Image Classification | 16 | 2.84% |
Denoising | 14 | 2.48% |
Image Restoration | 13 | 2.30% |
Image Segmentation | 12 | 2.13% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |