Geometry-guided Mamba enhances CNN semantic segmentation models

Researchers have adapted a geometry-guided Mamba model, originally from DGM-Net, to serve as a plug-and-play context module for CNN-based semantic segmentation. This approach injects geometric guidance into the selective scan process, enabling long-range feature propagation modulated by boundary and centripetal-flow cues. When integrated into six different CNN segmentation models, the geometry-guided SSM modules consistently improved mean Intersection over Union (mIoU) scores on the Cityscapes dataset with only a slight increase in computational cost. AI

IMPACT Enhances existing CNN segmentation models with improved context aggregation, potentially leading to more accurate image analysis in computer vision tasks.

RANK_REASON This is a research paper detailing a novel method for improving existing models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Cityscapes
• CNN
• DANet
• DGM-Net
• Geometry-Guided Mamba
• OCRNet
• PSPNet
• ResNet-101

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →