  Cross-view video geo-localization (CVGL) aims to derive GPS trajectories from
street-view videos by aligning them with aerial-view images. Despite their
promising performance, current CVGL methods face significant challenges. These
methods use camera and odometry data, typically absent in real-world scenarios.
They utilize multiple adjacent frames and various encoders for feature
extraction, resulting in high computational costs. Moreover, these approaches
independently predict each street-view frame's location, resulting in
temporally inconsistent GPS trajectories. To address these challenges, in this
work, we propose GAReT, a fully transformer-based method for CVGL that does not
require camera and odometry data. We introduce GeoAdapter, a
transformer-adapter module designed to efficiently aggregate image-level
representations and adapt them for video inputs. Specifically, we train a
transformer encoder on video frames and aerial images, then freeze the encoder
to optimize the GeoAdapter module to obtain video-level representation. To
address temporally inconsistent trajectories, we introduce TransRetriever, an
encoder-decoder transformer model that predicts GPS locations of street-view
frames by encoding top-k nearest neighbor predictions per frame and
auto-regressively decoding the best neighbor based on the previous frame's
predictions. Our method's effectiveness is validated through extensive
experiments, demonstrating state-of-the-art performance on benchmark datasets.
Our code is available at https://github.com/manupillai308/GAReT.
