学习漫游：通过可操作推理提升LMMs的全球图像地理定位能力
Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning
AI总结：
通过可操作推理提升LMMs的全球图像地理定位能力，提出GeoAoT框架和WanderBench基准，实现交互式地理定位探索。
摘要：
地理定位任务需要大量的世界知识和复杂的推理能力。尽管先进的大多模态模型（LMMs）已显示出上述优越能力，但其在地理定位任务上的表现仍未被探索。为此，我们引入了WanderBench，这是第一个开放获取的全球地理定位基准，专门用于具身场景中的可操作地理定位推理。WanderBench包含超过32,000个全景图，涵盖六个大洲，组织成可导航的图谱，使旋转和移动等物理动作成为可能，将地理定位从静态识别转变为交互式探索。在此基础上，我们提出了GeoAoT（Thought of Action），一种具有地理定位框架的行动推理方法，将推理与具身动作相结合。不同于生成文本推理链，GeoAoT生成可操作的计划，如接近地标或调整视角，以主动减少不确定性。我们进一步建立了一个评估协议，共同测量地理定位的准确性和难度感知的地理定位提问能力。在19个大型多模态模型上的实验表明，GeoAoT在细粒度定位和动态环境中的泛化能力更强。WanderBench和GeoAoT为具身视觉理解中的可操作、推理驱动的地理定位定义了新范式。
英文摘要：
Geolocation, the task of identifying the geographic location of an image, requires abundant world knowledge and complex reasoning abilities. Though advanced large multimodal models (LMMs) have shown superior aforementioned capabilities, their performance on the geolocation task remains unexplored. To this end, we introduce \textbf{WanderBench}, the first open access global geolocation benchmark designed for actionable geolocation reasoning in embodied scenarios. WanderBench contains over 32K panoramas across six continents, organized as navigable graphs that enable physical actions such as rotation and movement, transforming geolocation from static recognition into interactive exploration. Building on this foundation, we propose \textbf{GeoAoT} (Action of Thought), a \underline{Geo}location framework with \underline{A}ction of \underline{T}hough, which couples reasoning with embodied actions. Instead of generating textual reasoning chains, GeoAoT produces actionable plans such as, approaching landmarks or adjusting viewpoints, to actively reduce uncertainty. We further establish an evaluation protocol that jointly measures geolocation accuracy and difficulty-aware geolocation questioning ability. Experiments on 19 large multimodal models show that GeoAoT achieves superior fine-grained localization and stronger generalization in dynamic environments. WanderBench and GeoAoT define a new paradigm for actionable, reasoning driven geolocation in embodied visual understanding.
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。
