From Visual Primitives to Semantic Masks: Fine-Grained Visual-Linguistic Alignment for Open-Vocabulary Remote Sensing Image Segmentation

Open-vocabulary semantic segmentation (OVSS) makes seg-mentation driven by text descriptions, enabling generalization to novelclasses. However, fine-grained OVSS in remote sensing is challenged byvisual similarity and semantic ambiguity, with existing methods lackingexplicit fine-grained visual-linguistic alignment and relying on a singleforward pass. To address these issues, we propose SANO3, a training-free framework that leverages refined textual and spatial guidance toboost the performance of SAM 3. Leveraging the visual self-similaritypriors of vision foundation models, we extract discriminative visual prim-itives and align them with text through visual-conditioned relation-awarealignment strategy. Next, we enhance text representations with hybridprototype injection for deep visual-linguistic fusion. To refine bound-aries, we treat spatial prompts generation as an iterative optimizationprocess, gradually selecting the most informative points via a greedy en-tropy reduction strategy. Further, we introduce FG-OVSSRS Bench, thefirst fine-grained remote sensing OVSS benchmark, encompassing 8 di-verse datasets across different sensors and domains. Evaluated on thesedatasets, SANO3 outperforms existing approaches and achieves state-of-the-art performance.