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.

翻译：通道注意力机制是一种在深度卷积神经网络中广泛采用的有效技术，用于提升图像处理任务（如图像分类和图像超分辨率）的性能。它通常被设计为参数化子网络，嵌入到网络的卷积层中，以学习更强大的特征表示。然而，当前的通道注意力机制会引入更多参数，从而导致更高的计算成本。为解决这一问题，本文提出了一种无参数通道注意力模块，旨在提升主流图像分类和超分辨率网络的性能，同时完全消除通道注意力带来的参数增长。在CIFAR-100、ImageNet和DIV2K上的实验验证表明，我们的PFCA模块在提升基于ResNet的图像分类性能和基于MSRResNet的图像超分辨率性能的同时，仅带来极少的参数与浮点运算量增长。