References & Citations
Computer Science > Computer Vision and Pattern Recognition
Title: Frequency-Adaptive Dilated Convolution for Semantic Segmentation
(Submitted on 8 Mar 2024 (v1), last revised 1 Apr 2024 (this version, v5))
Abstract: 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 this https URL
Submission history
From: Linwei Chen [view email][v1] Fri, 8 Mar 2024 15:00:44 GMT (22644kb)
[v2] Tue, 12 Mar 2024 08:33:51 GMT (22645kb,D)
[v3] Mon, 18 Mar 2024 01:53:42 GMT (22645kb,D)
[v4] Thu, 28 Mar 2024 13:41:05 GMT (22646kb)
[v5] Mon, 1 Apr 2024 07:26:06 GMT (22646kb)
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