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frequency-balanced feature

A frequency-balanced feature is an intermediate data representation in deep learning and computer vision in which different spatial frequency components, such as fine high-frequency details and broad low-frequency structures, are adaptively reweighted across an image. Rather than maintaining a fixed spectral distribution across all locations, this representation modulates the proportion of low- and high-frequency content based on local context, often suppressing irrelevant high frequencies in uniform background regions while preserving them around detailed boundaries and objects. By regulating the frequency spectrum spatially, frequency-balanced features enable neural network operations, such as dilated convolutions, to expand their effective receptive fields and capture wide contextual information without losing critical edge details or suffering from spectral artifacts.

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Frequency-Adaptive Dilated Convolution for Semantic Segmentation

Frequency-Adaptive Dilated Convolution for Semantic Segmentation

Linwei Chen, Lin Gu, Dezhi Zheng, Ying Fu

OrganizationsBeijing Institute of TechnologyRIKENUniversity of Tokyo

Why you should read this

Proposes Frequency-Adaptive Dilated Convolution to dynamically adjust dilation rates and kernel weights based on local spectral analysis, mitigating aliasing artifacts while balancing receptive field size and effective bandwidth in semantic segmentation.

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 perspective 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 made publicly available at https://github.com/ying-fu/FADC.

Added

2026-09-26