AHLFNet: Adaptive High–Low Frequency Collaborative Auxiliary Feature Alignment Network
Year: 2025
Type: article
Abstract: Dense image prediction tasks require both strong semantic category information and precise boundary delineation in order to be effectively applied to downstream applications. However, existing networks typically fuse deep coarse features with adjacent fine features directly through upsampling. Such a straightforward upsampling strategy not only blurs boundaries due to the loss of high-frequency information, but also amplifies intra-class conflicts caused by high-frequency interference within the same object. To address these issues, this paper proposes an Adaptive High–Low Frequency Collaborative Auxiliary Feature Alignment Network(AHLFNet), which consists of an Adaptive Low-Frequency Multi-Kernel Smoothing Unit(ALFU), a Gate-Controlled Selector(GCS), and an Adaptive High-Frequency Edge Enhancement Unit(AHFU). The ALFU suppresses high-frequency components within objects, mitigating interference during upsampling and thereby reducing intra-class conflicts. The GCS adaptively chooses suitable convolutional kernels based on the size of similar regions to ensure accurate upsampled features. The AHFU preserves high-frequency details from low-level features, enabling more refined boundary delineation. Extensive experiments demonstrate that the proposed network achieves state-of-the-art performance across various downstream tasks.
Source: Symmetry
DOI: 10.3390/sym17111952
Authors: Chunguang Yue, Jinbao Li
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416231041