Figure 3.

MSI module structural diagram. Given input features, scAMAC applies global average pooling to obtain a summary representation. Subsequently, it utilizes fast 1D convolution to capture inter-channel interaction information. By applying the sigmoid function, it generates channel weights that indicate the importance of each channel. Finally, these weights are used to combine the input feature map in a weighted sum operation.