Recent researches indicate that utilizing the frequency information of input data can enhance the performance of networks. However, the existing popular convolutional structure is not designed specifically for utilizing the frequency information contained in datasets. In this paper, we propose a novel and effective module, named FreConv (frequency branch-and-integration convolution), to replace the vanilla convolution. FreConv adopts a dual-branch architecture to extract and integrate high- and low-frequency information. In the high-frequency branch, a derivative-filter-like architecture is designed to extract the high-frequency information while a light extractor is employed in the low-frequency branch because the low-frequency information is usually redundant. FreConv is able to exploit the frequency information of input data in a more reasonable way to enhance feature representation ability and reduce the memory and computational cost significantly. Without any bells and whistles, experimental results on various tasks demonstrate that FreConv-equipped networks consistently outperform state-of-the-art baselines.

翻译：近期研究表明，利用输入数据的频率信息可提升网络性能。然而，现有主流卷积结构并非专为利用数据集中蕴含的频率信息而设计。本文提出一种新颖且有效的模块——FreConv（频率分支与集成卷积），用以替代标准卷积。FreConv采用双分支架构提取并融合高频与低频信息。在高频分支中，设计类导数滤波器结构提取高频信息；低频分支则采用轻量提取器，因低频信息通常存在冗余。FreConv能以更合理的方式利用输入数据的频率信息，增强特征表示能力并显著降低内存与计算成本。无需复杂技巧，多项任务实验结果显示，配备FreConv的网络持续优于最先进的基线方法。