  Dilated convolution, which expands the receptive field by inserting gaps
between its consecutive elements, is widely employed in computer vision. In
this study, we propose three strategies to improve individual phases of dilated
convolution from the view of spectrum analysis. Departing from the conventional
practice of fixing a global dilation rate as a hyperparameter, we introduce
Frequency-Adaptive Dilated Convolution (FADC), which dynamically adjusts
dilation rates spatially based on local frequency components. Subsequently, we
design two plug-in modules to directly enhance effective bandwidth and
receptive field size. The Adaptive Kernel (AdaKern) module decomposes
convolution weights into low-frequency and high-frequency components,
dynamically adjusting the ratio between these components on a per-channel
basis. By increasing the high-frequency part of convolution weights, AdaKern
captures more high-frequency components, thereby improving effective bandwidth.
The Frequency Selection (FreqSelect) module optimally balances high- and
low-frequency components in feature representations through spatially variant
reweighting. It suppresses high frequencies in the background to encourage FADC
to learn a larger dilation, thereby increasing the receptive field for an
expanded scope. Extensive experiments on segmentation and object detection
consistently validate the efficacy of our approach. The code is publicly
available at https://github.com/Linwei-Chen/FADC.
