  Visual perception is driven by the focus on relevant aspects in the
surrounding world. To transfer this observation to the digital information
processing of computers, attention mechanisms have been introduced to highlight
salient image regions. Here, we introduce a parameter-free attention mechanism
called PfAAM, that is a simple yet effective module. It can be plugged into
various convolutional neural network architectures with a little computational
overhead and without affecting model size. PfAAM was tested on multiple
architectures for classification and segmentic segmentation leading to improved
model performance for all tested cases. This demonstrates its wide
applicability as a general easy-to-use module for computer vision tasks. The
implementation of PfAAM can be found on https://github.com/nkoerb/pfaam.
