Finally, the activation maps of the preceding convolutional block are then reweighted (using the 1 × 128 outputs provided by the excitation layer as weights) to generate the output of the SE block, which can then be fed directly into subsequent layers.
sE blocks are just alternative pathways designed to recalibrate channelwise feature responses by explicitly modeling interdependencies between channels.