GeoVista: 用于超高清遥感理解的视觉引导主动感知
GeoVista: Visually Grounded Active Perception for Vision-Language Understanding of Ultra-High-Resolution Remote Sensing Images
AI总结：
GeoVista提出一种基于规划的主动感知框架，通过构建全局探索计划和分支式局部检查，实现超高清遥感图像的高效解读，提升全局上下文理解与证据聚合效率。
摘要：
解读超高清（UHR）遥感图像需要模型在大规模场景中搜索稀疏且微小的视觉证据。现有遥感视觉-语言模型虽然能通过缩放和裁剪工具检查局部区域，但大多数探索策略局限于单一聚焦或单一轨迹，易丢失全局上下文、遗漏散区域并重复计数证据。为此，我们提出GeoVista，一种基于规划的主动感知框架，通过构建全局探索计划，通过分支式局部检查验证多个候选区域，并维护显式的证据状态以实现跨区域聚合与去重。为实现此行为，我们引入APEX-GRO，一个冷启动监督轨迹语料库，将多样化的UHR任务重新表述为全局-区域-对象交互推理过程，采用统一且尺度不变的空间表示。我们进一步设计观察-规划-跟踪机制，用于全局观察、自适应区域检查和证据跟踪，并通过逐步奖励对齐模型与基于GRPO的策略，用于规划、定位和最终答案正确性。在RSHR-Bench、XLRS-Bench和LRS-VQA上的实验表明，GeoVista实现了最先进的性能。代码和数据集可在https://github.com/ryan6073/GeoVista获取。
英文摘要：
Interpreting ultra-high-resolution (UHR) remote sensing images requires models to search for sparse and tiny visual evidence across large-scale scenes. Existing remote sensing vision-language models can inspect local regions with zooming and cropping tools, but most exploration strategies follow either a one-shot focus or a single sequential trajectory. Such single-path exploration can lose global context, leave scattered regions unvisited, and revisit or count the same evidence multiple times. To this end, we propose GeoVista, a planning-driven active perception framework for UHR remote sensing interpretation. Instead of committing to one zooming path, GeoVista first builds a global exploration plan, then verifies multiple candidate regions through branch-wise local inspection, while maintaining an explicit evidence state for cross-region aggregation and de-duplication. To enable this behavior, we introduce APE-GRO, a cold-start supervised trajectory corpus that reformulates diverse UHR tasks as Global-Region-Object interactive reasoning processes with a unified, scale-invariant spatial representation. We further design an Observe-Plan-Track mechanism for global observation, adaptive region inspection, and evidence tracking, and align the model with a GRPO-based strategy using step-wise rewards for planning, localization, and final answer correctness. Experiments on RSHR-Bench, XLRS-Bench, and LRS-VQA show that GeoVista achieves state-of-the-art performance. Code and dataset are available at https://github.com/ryan6073/GeoVista.
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。
