BGG adapts vision foundation models using multi-granularity dilated convolutions and frequency-domain patch aggregation to achieve state-of-the-art cross-view geo-localization on University-1652 and SUES-200 with low training cost.
University-1652: A multi-view multi- source benchmark for drone-based geo-localization
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BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization
BGG adapts vision foundation models using multi-granularity dilated convolutions and frequency-domain patch aggregation to achieve state-of-the-art cross-view geo-localization on University-1652 and SUES-200 with low training cost.