SRM
style-based recalibration module
SRM combines style transfer with an attention mechanism. Its main contribution is style pooling which utilizes both mean and standard deviation of the input features to improve its capability to capture global information. It also adopts a lightweight channel-wise fully-connected (CFC) layer, in place of the original fully-connected layer, to reduce the computational requirements.
Given an input feature map $X \in \mathbb{R}^{C \times H \times W}$, SRM first collects global information by using style pooling ($\text{SP}(\cdot)$) which combines global average pooling and global standard deviation pooling. 
Then a channel-wise fully connected ($\text{CFC}(\cdot)$) layer (i.e. fully connected per channel), batch normalization $\text{BN}$ and sigmoid function $\sigma$ are used  to provide the attention vector. Finally,   as in an SE block, the input features are multiplied by the attention vector. Overall, an SRM can be written as:
\begin{align}
    s = F_\text{srm}(X, \theta) & = \sigma (\text{BN}(\text{CFC}(\text{SP}(X))))
\end{align}
\begin{align}
    Y & = s  X
\end{align}
The SRM block improves both squeeze and excitation modules, yet can be added after each residual unit like an SE block.
SRM: A Style-Based Recalibration Module for Convolutional Neural Networks
https://paperswithcode.com/paper/srm-a-style-based-recalibration-module-for-1
SRM: A Style-Based Recalibration Module for Convolutional Neural Networks