Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition
Urban-scale Visual Place Recognition (VPR) aims to identify the geographic location of a query image by matching it against a geo-tagged database. However, recent methods overlook a serious long-tailed problem in urban datasets, biasing models toward well-photographed areas while failing in sparsely covered regions. This paper systematically characterizes this imbalance and proposes Distribution-Aware Place Recognition (DAPR), a model-agnostic plug-in framework that rebalances gradient contributions across head and tail classes. 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 SF-XL benchmark, DAPR outperforms the previous baseline by 18.3% on test set v1 and 6.7% on v2, and achieves consistent improvements across methods and benchmarks.
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[Submitted on 30 Jun 2026]
Title:Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition
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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.
Comments: Accepted to ECCV 2026, 28 pages including supplementary material
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
ACM classes: I.4; I.2.10; I.5
Cite as: arXiv:2607.00090 [cs.CV]
(or arXiv:2607.00090v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.00090
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zoey Shu [view email] [v1] Tue, 30 Jun 2026 19:33:39 UTC (11,971 KB)
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