LaViSE: Language-aware Vision Scale Enhancement for Referring Remote Sensing Image Segmentation

Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in aerial imagery based on natural language expressions. Although recent multi-scale feature aggregation methods have improved cross-modal alignment, and existing approaches still struggle with accurate localization and segmentation across scales because interactions between visual scales and language are not sufficiently modeled. To address these challenges, we propose a SAM-based framework termed LaViSE, which incorporates two key modules : the Language-Guided Hierarchical Fusion (LGHF) module integrates cross-modal features at multiple scales for precise object localization by injecting spatial coordinates into visual representations and combining visual features with aligned features; the Language-Attentive Scale-Unified Aggregation (LASA) module globally merges multi-level features while maintaining spatial consistency and boundary fidelity, and further preserves fine-grained structural details through and language-conditioned feature recalibration across scales, which is crucial for segmenting densely distributed and scale-varying targets in complex remote sensing imagery. Experiments on the widely used RefSegRS and RRSIS-D benchmarks demonstrate that LaViSE consistently outperforms state-of-the-art methods, particularly in challenging scenarios with small and densely distributed objects. Our code and pre-trained models will be released upon publication.