UniEvo-RS：基于代表性示例驱动原型演化的全提示统一遥感分割
UniEvo-RS: Omni-Prompt Unified Remote Sensing Segmentation with Representative Exemplar-Driven Prototype Evolution
AI总结：
UniEvo-RS是配备代表性示例驱动原型演化的全提示统一遥感分割框架，通过多指令提示数据集与原型演化机制，在批量标注中对未见过的类别实现无需训练的精度提升，性能达当前最优。
AI中文摘要：
基于提示的视觉语言模型（VLMs）在加速密集遥感（RS）标注方面具有巨大潜力，但静态模型在部署到新场景、未见过的类别或视觉混淆背景时会出现严重的性能下降。此外，现有的统一范式主要依赖图像内特定提示，缺乏灵活的任务路由以适应多意图操作工作流。在实际的批量制图中，标注者通常在处理大型数据集之前先精炼一小部分代表性样本。受此实践启发，我们提出UniEvo-RS，这是一个配备了代表性示例驱动原型演化的全提示统一RS分割框架。首先，我们构建了一个多指令提示数据集，在单一架构内统一了文本驱动和视觉驱动的提示，为高度多样化的RS标注场景建立了动态任务路由机制。其次，我们引入了一种代表性反馈驱动、无需训练的原型演化机制。通过对比示例上的手动标注与初始预测，UniEvo-RS将预测错误提炼为正、负原型。这些原型在固定预算的聚类内存下增强了LLM查询的召回率并抑制了空间背景噪声。大量实验表明，UniEvo-RS统一了多样化的提示任务，在大多数设置中取得了最先进的性能。至关重要的是，通过对少量示例的最小交互，它能在批量标注期间对未见过的类别实现无需训练的渐进式精度提升。
英文摘要：
Prompt-driven vision-language models (VLMs) hold immense promise for accelerating dense remote sensing (RS) annotation, but static models suffer from severe performance degradation when deployed on novel scenes, unseen categories, or visually confusing backgrounds. Moreover, existing unified paradigms primarily rely on intra-image specific prompts, lacking flexible task routing to adapt to multi-intent operational workflows. In practical batch mapping, annotators typically refine a small set of representative samples before processing large datasets. Motivated by this practice, we propose UniEvo-RS, an omni-prompt unified RS segmentation framework equipped with representative exemplar-driven prototype evolution. First, we construct a multi-instruction prompt dataset that unifies text-driven and visual-driven prompts within a single architecture, establishing a dynamic task-routing mechanism for highly diverse RS annotation scenarios. Second, we introduce a representative feedback-driven, training-free prototype evolution mechanism. By contrasting manual annotations with initial predictions on exemplars, UniEvo-RS distills prediction errors into positive and negative prototypes. These prototypes enhance LLM query recall and suppress spatial background noise under a fixed-budget clustering memory. Extensive experiments show that UniEvo-RS unifies diverse prompting tasks, achieving state-of-the-art performance across most settings. Crucially, with minimal interaction on a few exemplars, it enables training-free, progressive accuracy enhancement on unseen categories during batch annotation.
