MOGeo：超越一对一跨视角目标地理定位
MOGeo: Beyond One-to-One Cross-View Object Geo-localization
论文提出跨视角多目标地理定位任务并构建基准数据集，为视觉定位研究提供了更贴近现实的多目标设定和评测协议，可推动多目标跨视角匹配方法的发展。
原文依据 we propose a new task, called Cross-View Multi-Object Geo-Localization (CVMOGL). To advance the CVMOGL task, we first construct a benchmark, CMLocation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论跨视角目标地理定位（CVOGL）旨在在对应的卫星图像中定位查询图像内的感兴趣目标。现有方法通常假设查询图像仅包含单个目标，这与现实应用中复杂的多目标地理定位需求不符，导致其不适用于实际场景。为弥合现实设定与现有任务之间的差距，我们提出了一项新任务，称为跨视角多目标地理定位（CVMOGL）。为推进CVMOGL任务，我们首先构建了一个基准CMLocation，包含两个数据集：CMLocation-V1和CMLocation-V2。此外，我们提出了一种新颖的跨视角多目标地理定位方法MOGeo，并与现有最优方法进行了基准对比。我们在多种应用场景下开展了大量实验，验证了所提方法的有效性。结果表明，在更现实的设定下，跨视角目标地理定位仍是一个具有挑战性的问题，值得进一步研究。我们的数据集和代码将发布于 https://github.com/LV-BO001/MOGeo 。
英文原文摘要 1180 字符
Cross-View Object Geo-Localization (CVOGL) aims to locate an object of interest in a query image within a corresponding satellite image. Existing methods typically assume that the query image contains only a single object, which does not align with the complex, multi-object geo-localization requirements in real-world applications, making them unsuitable for practical scenarios. To bridge the gap between the realistic setting and existing task, we propose a new task, called Cross-View Multi-Object Geo-Localization (CVMOGL). To advance the CVMOGL task, we first construct a benchmark, CMLocation, which includes two datasets: CMLocation-V1 and CMLocation-V2. Furthermore, we propose a novel cross-view multi-object geo-localization method, MOGeo, and benchmark it against existing state-of-the-art methods. Extensive experiments are conducted under various application scenarios to validate the effectiveness of our method. The results demonstrate that cross-view object geo-localization in the more realistic setting remains a challenging problem, encouraging further research in this area. Our dataset and code will be released at \texttt https://github.com/LV-BO001/MOGeo .