{"ID":2841215,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12054","arxiv_id":"2511.12054","title":"UniABG: Unified Adversarial View Bridging and Graph Correspondence for Unsupervised Cross-View Geo-Localization","abstract":"Cross-view geo-localization (CVGL) matches query images ($\\textit{e.g.}$, drone) to geographically corresponding opposite-view imagery ($\\textit{e.g.}$, satellite). While supervised methods achieve strong performance, their reliance on extensive pairwise annotations limits scalability. Unsupervised alternatives avoid annotation costs but suffer from noisy pseudo-labels due to intrinsic cross-view domain gaps. To address these limitations, we propose $\\textit{UniABG}$, a novel dual-stage unsupervised cross-view geo-localization framework integrating adversarial view bridging with graph-based correspondence calibration. Our approach first employs View-Aware Adversarial Bridging (VAAB) to model view-invariant features and enhance pseudo-label robustness. Subsequently, Heterogeneous Graph Filtering Calibration (HGFC) refines cross-view associations by constructing dual inter-view structure graphs, achieving reliable view correspondence. Extensive experiments demonstrate state-of-the-art unsupervised performance, showing that UniABG improves Satellite $\\rightarrow$ Drone AP by +10.63\\% on University-1652 and +16.73\\% on SUES-200, even surpassing supervised baselines. The source code is available at https://github.com/chenqi142/UniABG","short_abstract":"Cross-view geo-localization (CVGL) matches query images ($\\textit{e.g.}$, drone) to geographically corresponding opposite-view imagery ($\\textit{e.g.}$, satellite). While supervised methods achieve strong performance, their reliance on extensive pairwise annotations limits scalability. Unsupervised alternatives avoid a...","url_abs":"https://arxiv.org/abs/2511.12054","url_pdf":"https://arxiv.org/pdf/2511.12054v1","authors":"[\"Cuiqun Chen\",\"Qi Chen\",\"Bin Yang\",\"Xingyi Zhang\"]","published":"2025-11-15T06:32:52Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":607039,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2841215,"paper_url":"https://arxiv.org/abs/2511.12054","paper_title":"UniABG: Unified Adversarial View Bridging and Graph Correspondence for Unsupervised Cross-View Geo-Localization","repo_url":"https://github.com/chenqi142/UniABG","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}