论文标题
通过通道重新评估注意模块优化卷积神经网络
Convolutional Neural Network optimization via Channel Reassessment Attention module
论文作者
论文摘要
可以通过通过注意机制调整通道之间的相互关系来改善卷积神经网络（CNN）的性能。但是，最近进步中的注意力机制尚未完全利用特征图的空间信息，这对生成的通道兴奋剂的结果产生了很大的影响。在本文中，我们提出了一个新型的网络优化模块，称为通道重新评估注意力（CRA）模块，该模块使用具有特征图的空间信息的通道注意力，以增强网络的代表力。我们采用CRA模块根据不同通道中的特征图来评估通道的关注，然后通过频道关注和特征映射之间的产品适应最终特征。CRA模块是一个计算轻量级模块，可以将其嵌入到任何CNN的架构中。 ImageNet，CIFAR和MS可可数据集的实验表明，在不同的评估标准下，CRA模块的嵌入在各种网络上可以有效地提高性能。
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.
