FGLNet: frequency global and local context channel attention networks

The application of attention mechanisms, especially channel attention, has achieved huge success in the field of computer vision. However, existing methods mainly focus on more sophisticated attention modules for better performance, but ignore global and local contexts in the frequency domain. This work focuses on the channel relationship and proposes a novel architectural unit called Frequency Global and Local (FGL) context block. It adaptively recalibrates global-local channel-wise feature responses by explicitly modeling interdependencies between channels in the frequency domain. The proposed lightweight FGL module is efficient well generalizable across different datasets. Meanwhile, the FGL context block significantly improves the performance of existing convolutional neural networks (CNNs) at a slight computational cost. Our FGL module is extensively evaluated with applications of image classification, object detection, and semantic segmentation with the backbones of ResNets, MobileNetV2, and MobileNeXt. The experimental results indicate that our module is more efficient than its counterparts. Our model is open-sourced at https://github.com/YunDuanFei/FGL.