GeoBridge：一种语义锚定的多视角基础模型，桥接图像与文本以实现地理定位
GeoBridge: A Semantic-Anchored Multi-View Foundation Model Bridging Images and Text for Geo-Localization
用文本描述作为语义锚点桥接无人机、街景全景与卫星多视角特征，实现双向跨视角匹配与语言到图像检索；其语义锚点对齐机制可迁移到任何需要跨视角/跨模态特征对齐的检索任务。
原文依据 GeoBridge builds on a novel semantic-anchor mechanism that bridges multi-view features through textual descriptions for robust, flexible localization.
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地理定位通过检索与查询图像视觉对应的带地理标签参考图像来推断位置。然而，传统以卫星为中心的模式在高分辨率或最新卫星影像不可用时限制了鲁棒性，并且未能充分利用跨视角（如无人机、卫星与街景）和跨模态（如语言与图像）的互补线索。为应对这些挑战，我们提出 GeoBridge，一种新颖的模型，可执行跨视角双向匹配并支持语言到图像的检索。GeoBridge 超越传统以卫星为中心的表述，基于一种新颖的语义锚点机制，通过文本描述桥接多视角特征，从而实现鲁棒、灵活的定位。为支持该任务，我们构建了 GeoLoc，这是首个大规模、跨模态且多视角对齐的数据集，包含来自 36 个国家的超过 50,000 对无人机、街景全景与卫星图像及其文本描述，确保了地理与语义双重对齐。我们在多个任务上开展了广泛评估。实验证实，GeoLoc 预训练显著提升了 GeoBridge 的地理定位精度，同时促进了跨域泛化与跨模态知识迁移。代码、数据集与预训练模型将在 https://github.com/MiliLab/GeoBridge 发布。
英文原文摘要 1411 字符
Cross-view geo-localization infers a location by retrieving geo-tagged reference images that visually correspond to a query image. However, the traditional satellite-centric paradigm limits robustness when high-resolution or up-to-date satellite imagery is unavailable. It further underexploits complementary cues across views (e.g., drone, satellite, and street) and modalities (e.g., language and image). To address these challenges, we propose GeoBridge, a novel model that performs bidirectional matching across views and supports language-to-image retrieval. Going beyond traditional satellite-centric formulations, GeoBridge builds on a novel semantic-anchor mechanism that bridges multi-view features through textual descriptions for robust, flexible localization. In support of this task, we construct GeoLoc, the first large-scale, cross-modal, and multi-view aligned dataset comprising over 50,000 pairs of drone, street-view panorama, and satellite images as well as their textual descriptions, collected from 36 countries, ensuring both geographic and semantic alignment. We performed broad evaluations across multiple tasks. Experiments confirm that GeoLoc pre-training markedly improves geo-location accuracy for GeoBridge while promoting cross-domain generalization and cross-modal knowledge transfer. Code, dataset, and pretrained models will be released at https://github.com/MiliLab/GeoBridge.