Search papers, labs, and topics across Lattice.
This paper introduces DiCoR, a novel framework for referring remote sensing image segmentation that effectively decouples referent disambiguation from contour recalibration within an efficient joint fusion segmentation pipeline. By employing a disambiguation-aware localization strategy and a lightweight contour recalibration module, DiCoR enhances segmentation accuracy while maintaining computational efficiency. Experimental results reveal that DiCoR outperforms existing methods, achieving significant improvements in segmentation metrics while also being faster than traditional decoupled approaches.
Achieving a 5.28% improvement in segmentation accuracy while running 4.7% faster than existing methods, DiCoR redefines efficiency in referring remote sensing image segmentation.
Referring remote sensing image segmentation (RRSIS) aims to delineate targets specified by natural language expressions in remote sensing imagery. Existing methods mainly follow joint fusion segmentation (JFS) or decoupled prompt segmentation (DPS). JFS is efficient but often suffers from limited accuracy because referent localization and mask delineation are optimized under a unified objective, whereas DPS separates localization from mask generation using spatial prompts and foundation segmenters at the cost of higher memory consumption and inference latency. To bridge this gap, we propose DiCoR, a decoupled referent disambiguation and contour recalibration framework built on an efficient JFS pipeline. DiCoR addresses two key challenges: distinguishing the correct referent from ambiguous candidates and refining coarse masks after localization. A disambiguation-aware localization guidance strategy ranks salient candidate regions with adaptive linguistic cues and injects the resulting localization prior into fused features. A lightweight contour recalibration module further predicts residual corrections to coarse logits under localized contour supervision, improving mask quality with limited computational overhead. Experiments on RefSegRS, RRSIS-D, and RISBench show that DiCoR achieves the best segmentation accuracy across all three benchmarks. On RefSegRS, it improves mIoU and gIoU by 5.28% and 2.87% over a competitive JFS method while running 4.7% faster than a representative DPS method, demonstrating a favorable accuracy-efficiency trade-off. Code is available at https://github.com/zyGao1126/DiCoR.
