CRISP: Calibration-Aware Visual State Space Duality for Remote Sensing Semantic Segmentation
arXiv:2608.23746v1 Announce Type: new Abstract: 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.
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[Submitted on 24 Aug 2026]
Title:CRISP: Calibration-Aware Visual State Space Duality for Remote Sensing Semantic Segmentation
View a PDF of the paper titled CRISP: Calibration-Aware Visual State Space Duality for Remote Sensing Semantic Segmentation, by Kangning Wang and 2 other authors
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Abstract: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 this https URL.
Comments: Accepted to ECCV 2026. 22 pages, including supplementary material; 9 figures. Code: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.23746 [cs.CV]
(or arXiv:2608.23746v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.23746
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kangning Wang [view email] [v1] Mon, 24 Aug 2026 18:35:00 UTC (4,460 KB)
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