Worldwide geolocalization aims to locate the precise location at the
coordinate level of photos taken anywhere on the Earth. It is very challenging
due to 1) the difficulty of capturing subtle location-aware visual semantics,
and 2) the heterogeneous geographical distribution of image data. As a result,
existing studies have clear limitations when scaled to a worldwide context.
They may easily confuse distant images with similar visual contents, or cannot
adapt to various locations worldwide with different amounts of relevant data.
To resolve these limitations, we propose G3, a novel framework based on
Retrieval-Augmented Generation (RAG). In particular, G3 consists of three
steps, i.e., Geo-alignment, Geo-diversification, and Geo-verification to
optimize both retrieval and generation phases of worldwide geolocalization.
During Geo-alignment, our solution jointly learns expressive multi-modal
representations for images, GPS and textual descriptions, which allows us to
capture location-aware semantics for retrieving nearby images for a given
query. During Geo-diversification, we leverage a prompt ensembling method that
is robust to inconsistent retrieval performance for different image queries.
Finally, we combine both retrieved and generated GPS candidates in
Geo-verification for location prediction. Experiments on two well-established
datasets IM2GPS3k and YFCC4k verify the superiority of G3 compared to other
state-of-the-art methods.

提出了基于检索增强生成 (RAG) 的 G3 框架，通过全球地理位置定位的几个步骤以优化检索和生成阶段，该框架在全球上下文中优于其他先进方法。