Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition

• URL: http://arxiv.org/abs/2508.11497v1
• Date: Fri, 15 Aug 2025 14:19:50 GMT
• Title: Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition
• Authors: Feiyue Zhao, Zhichao Zhang,
• Abstract summary: Convolutional neural networks (CNNs) have demonstrated strong performance in visual recognition tasks.<n>We propose a novel framework that integrates graph-based rea soning into CNNs to enhance both structural awareness and feature representation.<n>The proposed HGFE module is lightweight, end-to-end trainable, and can be seamlessly integrated into standard CNN backbone networks.
• Score: 6.580655899524989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional neural networks (CNNs) have demonstrated strong performance in visual recognition tasks, but their inherent reliance on regular grid structures limits their capacity to model complex topological relationships and non-local semantics within images. To address this limita tion, we propose the hierarchical graph feature enhancement (HGFE), a novel framework that integrates graph-based rea soning into CNNs to enhance both structural awareness and feature representation. HGFE builds two complementary levels of graph structures: intra-window graph convolution to cap ture local spatial dependencies and inter-window supernode interactions to model global semantic relationships. Moreover, we introduce an adaptive frequency modulation module that dynamically balances low-frequency and high-frequency signal propagation, preserving critical edge and texture information while mitigating over-smoothing. The proposed HGFE module is lightweight, end-to-end trainable, and can be seamlessly integrated into standard CNN backbone networks. Extensive experiments on CIFAR-100 (classification), PASCAL VOC, and VisDrone (detection), as well as CrackSeg and CarParts (segmentation), validated the effectiveness of the HGFE in improving structural representation and enhancing overall recognition performance.

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