Convolutional Neural Network optimization via Channel Reassessment Attention module
This work addresses a specific bottleneck in CNN optimization for computer vision tasks, offering an incremental improvement.
The paper tackles the problem of underutilized spatial information in channel attention mechanisms for CNNs by proposing a Channel Reassessment Attention (CRA) module, which improves performance on datasets like ImageNet, CIFAR, and MS COCO.
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.