Parameter-efficient fine-tuning lets MLLMs serve as effective retrievers for natural-language-guided cross-view geo-localization, beating dual-encoder baselines on GeoText-1652 and CVG-Text while using far fewer trainable parameters.
A semantic-enhanced multi-modal remote sens- ing foundation model for earth observation.Nature Machine Intelligence, pages 1–15
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Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization
Parameter-efficient fine-tuning lets MLLMs serve as effective retrievers for natural-language-guided cross-view geo-localization, beating dual-encoder baselines on GeoText-1652 and CVG-Text while using far fewer trainable parameters.