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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11497 |
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| _version_ | 1866908491070832640 |
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| author | Zhao, Feiyue Zhang, Zhichao |
| author_facet | Zhao, Feiyue Zhang, Zhichao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition Zhao, Feiyue Zhang, Zhichao Computer Vision and Pattern Recognition 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. |
| title | Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.11497 |