通过聚类引导精炼和模型集成投票改进遥感中的视觉定位
Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting
AI总结：
提出两种视觉定位流程（SGR和CGR），结合遥感专用模型RemoteSAM和通用分割模型SAM3，并通过多模型集成投票提升定位精度。
摘要：
视觉定位旨在定位与自然语言描述对应的图像区域，是可解释视觉系统的关键组成部分。在遥感图像中，由于场景复杂、目标小且尺度变化大，定位尤为困难。依赖单一模型往往不足以应对这些多样化的挑战。在这项工作中，我们提出了两种定位流程，即序列定位精炼（SGR）和聚类感知定位精炼（CGR），它们结合了专门用于遥感的视觉定位模型RemoteSAM和强大的通用分割模型SAM3的互补优势。我们的方法首先使用RemoteSAM获得目标位置的初始估计，然后使用SAM3进行精炼，以产生更准确且空间一致的分割。此外，我们探索了一种基于六个不同能力的定位流程的多数投票集成策略。这种多模型框架提高了鲁棒性，并显著提升了定位精度。实验结果表明，所提出的流程和集成方法优于单个模型，从而产生更可靠和精确的视觉定位预测。
英文摘要：
Visual grounding aims to locate image regions that correspond to natural language descriptions and is a key component of interpretable vision systems. In remote sensing imagery, grounding is particularly challenging due to complex scenes, small objects, and large variations in scale. Relying on a single model is often insufficient to address these diverse challenges. In this work, we propose two grounding pipelines, Sequential Grounding Refinement (SGR) and Cluster-Aware Grounding Refinement (CGR), that combine the complementary strengths of RemoteSAM, a visual grounding model specialized for remote sensing, and SAM3, a powerful general-purpose segmentation model. Our approach first uses RemoteSAM to obtain an initial estimate of object location, which is then refined using SAM3 to produce more accurate and spatially consistent segmentations. Additionally, we explore an ensemble strategy based on majority voting across six diverse grounding pipelines, each with distinct capabilities. This multi-model framework improves robustness and significantly enhances localization accuracy. Experimental results demonstrate that the proposed pipelines and ensemble approach outperform individual models, leading to more reliable and precise visual grounding predictions.
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