AI 中文总结
该研究针对遥感交互式分割需大量点击的问题，提出ISRS-DETR框架，结合RF-DETR解码器与动态Top-K点击选择策略，实现一次点击跨同类目标传播，在三个基准上达到最优精度且大幅降低点击数。
AI 中文摘要
交互式分割通过让用户用少量点击勾勒目标，降低了像素级标注的高昂成本。但将该范式直接应用于遥感影像并非易事：超高分辨率、极小的目标尺寸及稀疏的空间分布均会降低分割质量。近期研究已解决了分辨率瓶颈，在遥感交互式分割（ISRS）任务中取得了颇具竞争力的结果，但这类研究将图像内同一类的所有实例视为单个目标，导致对一个目标的交互对同类相邻目标毫无作用，且每张图像可能需要多达40次点击才能获得满意的掩码，阻碍了这些框架的实用性。我们注意到，遥感场景中存在显著的强目标间相关性，即单个被点击的目标对同类其他目标具有高度信息价值。基于此，我们提出ISRS-DETR，这是一种检测引导式交互式分割框架，在训练和推理阶段均注入目标级证据。ISRS-DETR采用带有交互式分割骨干网络的RF-DETR解码器定位共现的同类目标，并引入动态Top-K点击选择策略，仅保留可靠的候选区域并将其转换为模拟点击，从而让一次用户交互可在整个类别中传播。在三个标准遥感基准上的实验表明，ISRS-DETR在达到最优精度的同时，大幅降低了每张图像的点击数（NoC-I）。所有代码和数据划分将在论文录用后发布，以支持可复现性。
英文摘要
Interactive segmentation reduces the prohibitive cost of pixel-level annotation by allowing users to delineate objects with a few clicks. However, applying this paradigm directly to remote sensing imagery is non-trivial: ultra-high resolutions, small object sizes, and sparse spatial distributions all degrade segmentation quality. Recent work has addressed the resolution barrier and achieved competitive results in interactive segmentation for remote sensing (ISRS). However, they treat all instances of a class within an image as a single objective target. Consequently, interactions spent on one object contribute nothing to its same-class neighbours, and satisfactory masks may demand up to 40 clicks per image, hindering the practicality of these frameworks. We observe that remote sensing scenes exhibit markedly strong inter-object correlation, meaning a single clicked object is highly informative about the rest of its category. Building on this, we propose ISRS-DETR, a detection-guided interactive segmentation framework that injects object-level evidence into both training and inference. Our ISRS-DETR employs an RF-DETR decoder with the interactive segmentation backbone to localise co-occurring same-class objects, and introduces a Dynamic Top-K Click Selection strategy that retains only reliable proposals and converts each into a simulated click, so one user interaction propagates across an entire class. Experiments on three standard remote sensing benchmarks show that ISRS-DETR achieves state-of-the-art accuracy while substantially reducing Number of Clicks per Image (NoC-I). All codes and data splits will be released for reproducibility upon acceptance.
