Breaking the Resource Wall: Geometry-Guided Sequence Modeling for Efficient Semantic Segmentation
High-performance semantic segmentation has achieved significant progress in recent years, often driven by increasingly large backbones and higher computational budgets. While effective, such approaches introduce substantial computational overhead and limit accessibility under constrained hardware settings. In this paper, we propose DGM-Net (Directional Geometric Mamba Network), an efficient architecture that improves modeling capability through structural design rather than increasing model capacity. We introduce Directional Geometric Mamba (G-Mamba), a linear-complexity O(N) operator as an alternative to conventional context modeling modules such as ASPP and PPM. To further enhance struc...
| Authors | Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 April 2026 |
| Last updated at source | 4 May 2026 |
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