Understanding What Is Not Said:Referring Remote Sensing Image Segmentation with Scarce Expressions
Kai Ye, Bowen Liu, Jianghang Lin, Jiayi Ji, Pingyang Dai, Liujuan Cao

TL;DR
This paper introduces a weakly supervised learning paradigm for remote sensing image segmentation using limited referring expressions, supported by theoretical analysis and a novel model that improves performance with noisy, weak supervision.
Contribution
It proposes WREL, a new weakly referring expression learning framework for remote sensing, and LRB-WREL, a model that refines weak references with a learnable bank and teacher-student training.
Findings
WREL approaches fully supervised performance with limited annotations.
LRB-WREL improves robustness and generalization under noisy supervision.
Theoretical analysis confirms performance bounds of mixed-referring training.
Abstract
Referring Remote Sensing Image Segmentation (RRSIS) aims to segment instances in remote sensing images according to referring expressions. Unlike Referring Image Segmentation on general images, acquiring high-quality referring expressions in the remote sensing domain is particularly challenging due to the prevalence of small, densely distributed objects and complex backgrounds. This paper introduces a new learning paradigm, Weakly Referring Expression Learning (WREL) for RRSIS, which leverages abundant class names as weakly referring expressions together with a small set of accurate ones to enable efficient training under limited annotation conditions. Furthermore, we provide a theoretical analysis showing that mixed-referring training yields a provable upper bound on the performance gap relative to training with fully annotated referring expressions, thereby establishing the validity…
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