  The channel attention mechanism is a useful technique widely employed in deep
convolutional neural networks to boost the performance for image processing
tasks, eg, image classification and image super-resolution. It is usually
designed as a parameterized sub-network and embedded into the convolutional
layers of the network to learn more powerful feature representations. However,
current channel attention induces more parameters and therefore leads to higher
computational costs. To deal with this issue, in this work, we propose a
Parameter-Free Channel Attention (PFCA) module to boost the performance of
popular image classification and image super-resolution networks, but
completely sweep out the parameter growth of channel attention. Experiments on
CIFAR-100, ImageNet, and DIV2K validate that our PFCA module improves the
performance of ResNet on image classification and improves the performance of
MSRResNet on image super-resolution tasks, respectively, while bringing little
growth of parameters and FLOPs.
