  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.
