ExACT: 基于示例驱动的校准精化用于遥感图像中免训练的视觉定位
ExACT: Exemplar-Driven Calibrated Refinement for Training-Free Visual Grounding in Remote Sensing Images
AI总结：
提出ExACT框架，通过一次性视觉提示机制弥合多模态大语言模型在遥感视觉定位中的模态差距，实现免训练的精确像素级定位。
摘要：
遥感视觉定位（RSVG）旨在利用自由形式的自然语言描述在高分辨率遥感图像中定位特定物体。尽管多模态大语言模型（MLLMs）的最新进展在开放词汇RSVG中显示出巨大潜力，但其免训练适应受到抽象语言语义与细粒度视觉线索之间模态差距的阻碍。在杂乱的遥感场景中，这种差距不可避免地导致严重的定位漂移。为弥合这一差距，我们提出Exemplar-driven Calibrated Refinement（ExACT），一种新颖的免训练框架，由一次性视觉提示机制驱动，显式提供判别性结构指导以实现精确的像素级定位。具体而言，我们提出基于视觉示例的校准器（VEC），从给定示例中提取细粒度视觉对应关系，以修正来自冻结MLLMs的粗略跨模态先验，有效抑制背景伪影并准确勾勒目标边界。随后，结构感知精化器（SAR）采用迭代合并与选择聚类策略，将校准后的先验整合为高质量的正负几何提示。这些提示进而引导Segment Anything Model（SAM）实现精确的像素级预测。大量实验证实了ExACT在现有免训练和弱监督方法中的优越性。
英文摘要：
Remote sensing visual grounding (RSVG) aims to locate specific objects in high-resolution RS imagery using free-form natural language descriptions. While recent advances in multimodal large language models (MLLMs) show great potential for such open-vocabulary RSVG, their training-free adaptation is hindered by the modality gap between abstract linguistic semantics and fine-grained visual cues. In cluttered RS scenes, this gap inevitably causes severe localization drift. To bridge this gap, we propose Exemplar-driven Calibrated Refinement (ExACT), a novel training-free framework driven by a one-shot visual prompting mechanism to explicitly provide discriminative structural guidance for precise pixel-level localization. Specifically, we propose a Vision Exemplar-based Calibrator (VEC) that extracts fine-grained visual correspondences from the given exemplar to rectify the rough cross-modal priors from frozen MLLMs, effectively suppressing background artifacts and accurately outlining target boundaries. Subsequently, a Structure-Aware Refiner (SAR) employs an iterative merge-and-select clustering strategy to consolidate the calibrated priors into high-quality positive and negative geometric prompts. These prompts then guide the Segment Anything Model (SAM) to achieve precise pixel-level predictions. Extensive experiments confirm the superiority of ExACT over existing training-free and weakly-supervised methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
