Convolutional neural network optimization via channel reassessment attention module

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 ignores the effects of different spatial locations in feature maps on attention weights, 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) which uses channel attentions with spatial information of feature maps to enhance representational power of networks. We employ CRA module to assess channel attentions in different channels, then adaptively refine the final features by channel multiplication between channel attentions and feature maps. CRA 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.