  Given a ground-level query image and a geo-referenced aerial image that
covers the query's local surroundings, fine-grained cross-view localization
aims to estimate the location of the ground camera inside the aerial image.
Recent works have focused on developing advanced networks trained with accurate
ground truth (GT) locations of ground images. However, the trained models
always suffer a performance drop when applied to images in a new target area
that differs from training. In most deployment scenarios, acquiring fine GT,
i.e. accurate GT locations, for target-area images to re-train the network can
be expensive and sometimes infeasible. In contrast, collecting images with
noisy GT with errors of tens of meters is often easy. Motivated by this, our
paper focuses on improving the performance of a trained model in a new target
area by leveraging only the target-area images without fine GT. We propose a
weakly supervised learning approach based on knowledge self-distillation. This
approach uses predictions from a pre-trained model as pseudo GT to supervise a
copy of itself. Our approach includes a mode-based pseudo GT generation for
reducing uncertainty in pseudo GT and an outlier filtering method to remove
unreliable pseudo GT. Our approach is validated using two recent
state-of-the-art models on two benchmarks. The results demonstrate that it
consistently and considerably boosts the localization accuracy in the target
area.
