{"ID":5552787,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-03T19:25:37.684514219Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00090","arxiv_id":"2607.00090","title":"Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition","abstract":"Urban-scale Visual Place Recognition (VPR) aims to identify the geographic location of a query image by matching it against a geo-tagged database. While recent methods achieve impressive performance, they overlook a serious long-tailed problem hidden in urban-scale datasets, which biases the model towards locations with abundant images and ignores less-visited areas, causing models to systematically favor frequently photographed locations while failing in sparsely covered areas. In this paper, we systematically characterize this imbalance challenge and propose Distribution-Aware Place Recognition (DAPR), a model-agnostic plug-in framework that rebalances gradient contributions across head and tail classes. Additionally, within classification-retrieval pipelines, DAPR applies a multi-scale distance search mechanism to compute per-class distributional compactness, providing complementary gains at the retrieval stage. On the large-scale SF-XL benchmark, our framework outperforms the previous classification-retrieval baseline by 18.3% on test set v1, and 6.7% on test set v2. As a plug-in module, it achieves consistent improvements across representative VPR methods on SF-XL, MSLS, and Pitts30k, demonstrating broad generalizability across different methods and benchmarks.","short_abstract":"Urban-scale Visual Place Recognition (VPR) aims to identify the geographic location of a query image by matching it against a geo-tagged database. While recent methods achieve impressive performance, they overlook a serious long-tailed problem hidden in urban-scale datasets, which biases the model towards locations wit...","url_abs":"https://arxiv.org/abs/2607.00090","url_pdf":"https://arxiv.org/pdf/2607.00090v1","authors":"[\"Zhiyao Shu\",\"Jiacheng Yang\",\"Yang Lu\",\"Waishan Qiu\",\"Chuan Li\",\"Da Chen\"]","published":"2026-06-30T19:33:39Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}