English(EN) Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation

几何引导的Mamba增强了CNN语义分割模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-09 04:00

研究人员已将最初来自DGM-Net的几何引导Mamba模型改编为CNN基础语义分割的即插即用上下文模块。该方法将几何引导注入选择性扫描过程，从而实现由边界和向心流线索调制的长距离特征传播。当集成到六种不同的CNN分割模型中时，几何引导的SSM模块在Cityscapes数据集上始终提高了平均交并比（mIoU）分数，而计算成本仅略有增加。 AI

影响通过改进的上下文聚合增强现有的CNN分割模型，有望在计算机视觉任务中实现更准确的图像分析。

排序理由这是一篇详细介绍改进现有模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen, Chia-Min Lin, Yung-Che Wang, Jen-Shiun Chiang · 2026-06-09 04:00

将几何引导的Mamba泛化为CNN语义分割的即插即用上下文模块

arXiv:2606.08866v1 Announce Type: new Abstract: CNN-based semantic segmentation networks usually rely on context heads such as ASPP, PPM, or attention modules to enlarge the receptive field. These heads are effective but may introduce heavy computation, memory cost, or boundary l…

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将几何引导的Mamba泛化为CNN语义分割的即插即用上下文模块

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