  Visual Geo-localization (VG) refers to the process to identify the location
described in query images, which is widely applied in robotics field and
computer vision tasks, such as autonomous driving, metaverse, augmented
reality, and SLAM. In fine-grained images lacking specific text descriptions,
directly applying pure visual methods to represent neighborhood features often
leads to the model focusing on overly fine-grained features, unable to fully
mine the semantic information in the images. Therefore, we propose a two-stage
training method to enhance visual performance and use contrastive learning to
mine challenging samples. We first leverage the multi-modal description
capability of CLIP (Contrastive Language-Image Pretraining) to create a set of
learnable text prompts for each geographic image feature to form vague
descriptions. Then, by utilizing dynamic text prompts to assist the training of
the image encoder, we enable the image encoder to learn better and more
generalizable visual features. This strategy of applying text to purely visual
tasks addresses the challenge of using multi-modal models for geographic
images, which often suffer from a lack of precise descriptions, making them
difficult to utilize widely. We validate the effectiveness of the proposed
strategy on several large-scale visual geo-localization datasets, and our
method achieves competitive results on multiple visual geo-localization
datasets. Our code and model are available at
https://github.com/Chain-Mao/ProGEO.
