标题： GeoExplorer：基于好奇心驱动探索的主动地理定位 显示英文标题

标题： GeoExplorer: Active Geo-localization with Curiosity-Driven Exploration

摘要： 主动地理定位（AGL）是将目标（以各种模态表示，例如航拍图像、地面图像或文本）定位在预定义的搜索区域内的任务。 当前方法将AGL视为一种基于距离的奖励的目标达成强化学习（RL）问题。 它们通过隐式学习来最小化与目标的相对距离来定位目标。 然而，当距离估计变得具有挑战性或遇到未见过的目标和环境时，由于训练期间学到的探索策略不够可靠，智能体会表现出鲁棒性和泛化能力下降的问题。 在本文中，我们提出了GeoExplorer，这是一种结合了通过内在奖励驱动的探索的AGL代理。 与基于距离的奖励不同，我们的基于好奇心的奖励是目标无关的，能够基于有效的环境建模进行稳健、多样且上下文相关的探索。 这些能力已在四个AGL基准测试中通过大量实验得到验证，证明了GeoExplorer在各种环境中的有效性和泛化能力，特别是在定位不熟悉的目标和环境方面。 显示英文摘要

摘要： Active Geo-localization (AGL) is the task of localizing a goal, represented in various modalities (e.g., aerial images, ground-level images, or text), within a predefined search area. Current methods approach AGL as a goal-reaching reinforcement learning (RL) problem with a distance-based reward. They localize the goal by implicitly learning to minimize the relative distance from it. However, when distance estimation becomes challenging or when encountering unseen targets and environments, the agent exhibits reduced robustness and generalization ability due to the less reliable exploration strategy learned during training. In this paper, we propose GeoExplorer, an AGL agent that incorporates curiosity-driven exploration through intrinsic rewards. Unlike distance-based rewards, our curiosity-driven reward is goal-agnostic, enabling robust, diverse, and contextually relevant exploration based on effective environment modeling. These capabilities have been proven through extensive experiments across four AGL benchmarks, demonstrating the effectiveness and generalization ability of GeoExplorer in diverse settings, particularly in localizing unfamiliar targets and environments.