Learning Visual Representation Clusters for Cross-View Geo-Location

Cross-view geo-location is a crucial research field that determines the geographic location from images taken from different viewpoints. It is often studied as a retrieval task, where the query images are with unknown locations, and the database includes images with geo-tags from a different platform. Learning image representations by neural networks is an important step, and one typical training method is using a classification loss, where cross-view images of the same locations are considered the same category. However, existing methods only focus on pushing the representation distances of different categories while ignoring the intra-category representation distances of samples from different platforms. Considering that controlling the intra-category distance can help to guide the model to extract compact category-sharing representations from cross-view images, we propose a categorized cluster loss to learn separate and compact representation clusters. Categorized cluster loss can supervise the network to learn invariant information from samples of different platforms by constraining both the inter-category and intra-category feature distances. Meanwhile, we design a category-view-stratified sampling strategy, which samples balanced inputs in terms of both category and view in each batch during the learning process. We implemented our approach with a lightweight OSNet-based network and achieved higher accuracy with fewer parameters on a typical and challenging cross-view geo-location dataset than most state-of-the-art (SOTA) methods.