LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild
<|reference_start|>LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild: Image geolocation is a critical task in various image-understanding applications. However, existing methods often fail when analyzing challenging, in-the-wild images. Inspired by the exceptional background knowledge of multimodal language models, we systematically evaluate their geolocation capabilities using a novel image dataset and a comprehensive evaluation framework. We first collect images from various countries via Google Street View. Then, we conduct training-free and training-based evaluations on closed-source and open-source multi-modal language models. we conduct both training-free and training-based evaluations on closed-source and open-source multimodal language models. Our findings indicate that closed-source models demonstrate superior geolocation abilities, while open-source models can achieve comparable performance through fine-tuning.<|reference_end|>
@article{wang2024llmgeo:,
    title={LLMGeo: Benchmarking Large Language Models on Image Geolocation
  In-the-wild},
    author={Zhiqiang Wang, Dejia Xu, Rana Muhammad Shahroz Khan, Yanbin Lin,
  Zhiwen Fan and Xingquan Zhu},
    journal={arXiv preprint arXiv:2405.20363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20363},
    primaryClass={cs.CV}
}