Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition

• URL: http://arxiv.org/abs/2607.00090v1
• Date: Tue, 30 Jun 2026 19:33:39 GMT
• Title: Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition
• Abstract summary: Urban-scale Visual Place Recognition aims to identify the geographic location of a query image by matching it against a geo-tagged database.<n>Recent methods achieve impressive performance, but overlook a serious long-tailed problem hidden in urban-scale datasets.<n>We propose Distribution-Aware Place Recognition (DAPR), a model-agnostic plug-in framework that rebalances gradient contributions across head and tail classes.
• Score: 9.31929142038003
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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