改进视觉状态空间模型，提升遥感分割边界精度。
CRISP: Calibration-Aware Visual State Space Duality for Remote Sensing Semantic Segmentation
- 通过残差注入与频率校准恢复局部对比度和边界响应。
- 在Potsdam等数据集上实现更高mF1与mIoU，参数约3000万。
- 适合关注遥感图像细节分割的研究者与工程应用。
视觉状态空间双重性（VSSD）作为Transformer的线性时间替代方案，在密集视觉任务中表现出高效性。然而我们发现，VSSD将空间上下文压缩为全局聚合，抑制高频响应，导致遥感语义分割中边界过度平滑。为此，提出CRISP校准框架，核心为双重校准算子（DCO），通过残差注入与频率校准在不改变线性复杂度的前提下恢复局部对比度与边界响应。为保留恢复的细节，设计正交多原型（OMP）头，每类使用多个正交约束原型以建模大类内差异。在Potsdam、Vaihingen和LoveDA数据集上的大量实验表明，仅约3000万参数下，CRISP在均值F1（mF）与均值交并比（mIoU）上持续提升，且性能优于当前最优方法。代码已开源。
原文摘要 · Abstract (English)
State space models, especially Visual State Space Duality (VSSD), have emerged as efficient linear-time alternatives to Transformers for dense visual tasks. However, we observe that VSSD compresses spatial context into a global aggregation that suppresses high-frequency responses, causing excessive boundary smoothing in remote sensing semantic segmentation. To address this, we propose CRISP, a calibration framework with two components. Its core, the Duality Calibration Operator (DCO), restores local contrast and boundary responses through residual injection and frequency calibration within the VSSD backbone, without altering its linear complexity. To retain the recovered detail, an Orthogonal Multi-Prototype (OMP) head assigns multiple orthogonally constrained prototypes per class to model large intra-class variance. Extensive experiments on Potsdam, Vaihingen, and LoveDA show that, with approximately 30M parameters, CRISP achieves consistent gains in mean F1 (mF) and mIoU while remaining competitive with state-of-the-art methods. Code is available at https://github.com/crazylifeha/CRISP.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。