WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909867162206208 |
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| author | Xu, Guoan Xiao, Yang Jia, Wenjing Gao, Guangwei Qi, Guo-Jun Lin, Chia-Wen |
| author_facet | Xu, Guoan Xiao, Yang Jia, Wenjing Gao, Guangwei Qi, Guo-Jun Lin, Chia-Wen |
| contents | While recent semantic segmentation networks heavily rely on powerful pretrained encoders, most employ simplistic decoders, leading to suboptimal trade-offs between semantic context and fine-grained detail preservation. To address this, we propose a novel decoder architecture, WaveSeg, which jointly optimizes feature refinement in spatial and wavelet domains. Specifically, high-frequency components are first learned from input images as explicit priors to reinforce boundary details at early stages. A multi-scale fusion mechanism, Dual Domain Operation (DDO), is then applied, and the novel Spectrum Decomposition Attention (SDA) block is proposed, which is developed to leverage Mamba's linear-complexity long-range modeling to enhance high-frequency structural details. Meanwhile, reparameterized convolutions are applied to preserve low-frequency semantic integrity in the wavelet domain. Finally, a residual-guided fusion integrates multi-scale features with boundary-aware representations at native resolution, producing semantically and structurally rich feature maps. Extensive experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches both quantitatively and qualitatively, achieving efficient and precise segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition Xu, Guoan Xiao, Yang Jia, Wenjing Gao, Guangwei Qi, Guo-Jun Lin, Chia-Wen Computer Vision and Pattern Recognition While recent semantic segmentation networks heavily rely on powerful pretrained encoders, most employ simplistic decoders, leading to suboptimal trade-offs between semantic context and fine-grained detail preservation. To address this, we propose a novel decoder architecture, WaveSeg, which jointly optimizes feature refinement in spatial and wavelet domains. Specifically, high-frequency components are first learned from input images as explicit priors to reinforce boundary details at early stages. A multi-scale fusion mechanism, Dual Domain Operation (DDO), is then applied, and the novel Spectrum Decomposition Attention (SDA) block is proposed, which is developed to leverage Mamba's linear-complexity long-range modeling to enhance high-frequency structural details. Meanwhile, reparameterized convolutions are applied to preserve low-frequency semantic integrity in the wavelet domain. Finally, a residual-guided fusion integrates multi-scale features with boundary-aware representations at native resolution, producing semantically and structurally rich feature maps. Extensive experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches both quantitatively and qualitatively, achieving efficient and precise segmentation. |
| title | WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.21079 |