CROSS: Cascaded Distillation and Dual-Constraint Grounding for Remote Sensing Referring Segmentation

• URL: http://arxiv.org/abs/2608.03147v2
• Date: Wed, 05 Aug 2026 06:08:54 GMT
• Title: CROSS: Cascaded Distillation and Dual-Constraint Grounding for Remote Sensing Referring Segmentation
• Abstract summary: Cross is a tightly integrated paradigm for referring Remote Sensing Image.<n>First, we introduce Linguistic-Guided Cascaded Distillation (LGCD) to bridge the architectural gap.<n>Second, Perspective-Spatial Contrastive Learning (PSCL) imposes cross-anchored constraints.
• Score: 15.230353995992246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Remote Sensing Image Segmentation (RRSIS) has achieved significant progress through the integration of VLMs and the Segment Anything Model (SAM). However, this progress largely relies on strong pre-trained capabilities, while leaving two fundamental limitations insufficiently addressed: (1) Architectural Weak-Coupling, where the unidirectional flow forces reliance on coarse VLM prompts and wastes SAM's pixel-level structural guidance, causing localization drift; and (2) Object-Centric Semantic Bias, where models overemphasize dominant object semantics while remaining insensitive to spatial reasoning crucial for RRSIS. Motivated by these observations, we propose CROSS, a tightly integrated paradigm for RRSIS. First, we introduce Linguistic-Guided Cascaded Distillation (LGCD) to bridge the architectural gap, which distills SAM's geometric affinities as soft regularizers into VLM intermediate layers, injecting dense structural priors to refine localization. Second, Perspective-Spatial Contrastive Learning (PSCL) imposes cross-anchored constraints by mining mask-filtered deceptive distractors and spatial-linguistic counterfactuals as hard negatives, explicitly shattering semantic shortcuts to enforce genuine logical consistency. Extensive experiments on RRSIS benchmarks demonstrate that CROSS achieves state-of-the-art performance and maintains precise localization even under severe spatial description perturbations, standing as a robust new paradigm for RRSIS.

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