[2601.23064] HierLoc: Hyperbolic Entity Embeddings for Hierarchical Visual Geolocation

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.23064 (cs) [Submitted on 30 Jan 2026 (v1), last revised 2 Mar 2026 (this version, v2)] Title:HierLoc: Hyperbolic Entity Embeddings for Hierarchical Visual Geolocation Authors:Hari Krishna Gadi, Daniel Matos, Hongyi Luo, Lu Liu, Yongliang Wang, Yanfeng Zhang, Liqiu Meng View a PDF of the paper titled HierLoc: Hyperbolic Entity Embeddings for Hierarchical Visual Geolocation, by Hari Krishna Gadi and 6 other authors View PDF HTML (experimental) Abstract:Visual geolocalization, the task of predicting where an image was taken, remains challenging due to global scale, visual ambiguity, and the inherently hierarchical structure of geography. Existing paradigms rely on either large-scale retrieval, which requires storing a large number of image embeddings, grid-based classifiers that ignore geographic continuity, or generative models that diffuse over space but struggle with fine detail. We introduce an entity-centric formulation of geolocation that replaces image-to-image retrieval with a compact hierarchy of geographic entities embedded in Hyperbolic space. Images are aligned directly to country, region, subregion, and city entities through Geo-Weighted Hyperbolic contrastive learning by directly incorporating haversine distance into the contrastive objective. This hierarchical design enables interpretable predictions and efficient inference with 240k entity embeddings instead of over 5 million image embedding...