Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation

Author:

Zhou Zhongliang1ORCID,Zhang Jielu1ORCID,Guan Zihan2ORCID,Hu Mengxuan2ORCID,Lao Ni3ORCID,Mu Lan1ORCID,Li Sheng2ORCID,Mai Gengchen1ORCID

Affiliation:

1. University of Georgia, Athens, GA, USA

2. University of Virginia, Charlottesville, VA, USA

3. Google LLC, Mountain View, CA, USA

Funder

National Science Foundation

Publisher

ACM

Reference50 articles.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023).

2. Recent Advances in Retrieval-Augmented Text Generation

3. End-to-End Object Detection with Transformers

4. Vicente Vivanco Cepeda, Gaurav Kumar Nayak, and Mubarak Shah. 2023. Geo-CLIP: Clip-Inspired Alignment between Locations and Images for EffectiveWorldwide Geo-localization. In Thirty-seventh Conference on Neural Information Processing Systems.

5. Changhao Chen, Bing Wang, Chris Xiaoxuan Lu, Niki Trigoni, and Andrew Markham. 2020. A survey on deep learning for localization and mapping: Towards the age of spatial machine intelligence. arXiv preprint arXiv:2006.12567 (2020).

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