Self-Calibrated Convolutions
Self-Calibrated Convolutions
Liu et al. presented self-calibrated convolution as a means to enlarge the receptive field at each spatial location. 

Self-calibrated convolution is used together with a standard convolution. It first divides the input feature $X$ into $X_{1}$ and $X_{2}$ in the channel domain. The self-calibrated convolution first uses average pooling to reduce the input size and enlarge the receptive field:
\begin{align}
T_{1} = AvgPool_{r}(X_{1}) 
\end{align}
where $r$ is the filter size and stride. Then a convolution is used to model the channel relationship and a bilinear interpolation operator $Up$ is used to upsample the feature map: 

\begin{align}
X'_{1} = \text{Up}(Conv_2(T_1))
\end{align}

Next, element-wise multiplication finishes the self-calibrated process:

\begin{align}
Y'_{1} = Conv_3(X_1) \sigma(X_1 + X'_1)
\end{align}

Finally, the output feature map of is formed:
\begin{align}
Y_{1} &= Conv_4(Y'_{1})
\end{align}
\begin{align}
Y_2 &= Conv_1(X_2)
\end{align}
\begin{align}
Y &= [Y_1; Y_2]
\end{align}
Such self-calibrated convolution can enlarge the receptive field of a network and improve its adaptability. It achieves excellent results in image classification and certain downstream tasks such as instance segmentation, object detection and keypoint detection.
Improving Convolutional Networks With Self-Calibrated Convolutions
https://paperswithcode.com/paper/improving-convolutional-networks-with-self
Improving Convolutional Networks With Self-Calibrated Convolutions