A novel channel attention module for integrity and importance of feature map

To enhance the performance of Convolutional Neural Networks(CNNs), channel attention mechanism is widely employed in CNNs recently. Most existing channel attention mechanisms assign weights to feature maps to capture the information, reflecting the relative importance of feature maps and thus improving the overall network performance. However, they bring higher latency for CNNs. Furthermore, many studies indicate that CNNs generate redundant feature maps during the convolution process, and they are similar and unimportant relatively. Empirically, we have observed that assigning weights between 0 and 1 to important feature maps can lead to distortion of their importance. In order to overcome the balance between performance and complexity while retaining information about important feature maps and learn the weights of unimportant feature maps effectively, we propose a novel Efficient Weight Learning Channel Attention(EWLCA) mechanism. The attention module effectively learns the weights of unimportant feature maps, which prevents distortion without adding high latency. Therefore, the proposed EWLCA has stronger representational ability. In addition, we demonstrate the effectiveness of our method through experiments on CIFAR100, ImageNet and VOC datasets, including comparison with other attention modules. On ImageNet classification, our EWLCA achieves a Top-1 accuracy of 81.44% in ResNet34, which is 1.28% higher than the original ResNet34.