  The performance of convolutional neural networks (CNNs) can be improved by
adjusting the interrelationship between channels with attention mechanism.
However, attention mechanism in recent advance has not fully utilized spatial
information of feature maps, which makes a great difference to the results of
generated channel attentions. In this paper, we propose a novel network
optimization module called Channel Reassessment Attention (CRA) module which
uses channel attentions with spatial information of feature maps to enhance
representational power of networks. We employ CRA module to assess channel
attentions based on feature maps in different channels, then the final features
are refined adaptively by product between channel attentions and feature
maps.CRA module is a computational lightweight module and it can be embedded
into any architectures of CNNs. The experiments on ImageNet, CIFAR and MS COCO
datasets demonstrate that the embedding of CRA module on various networks
effectively improves the performance under different evaluation standards.
