Summary of Img2loc: Revisiting Image Geolocalization Using Multi-modality Foundation Models and Image-based Retrieval-augmented Generation, by Zhongliang Zhou et al.
Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation
by Zhongliang Zhou, Jielu Zhang, Zihan Guan, Mengxuan Hu, Ni Lao, Lan Mu, Sheng Li, Gengchen Mai
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This AI research paper proposes a novel system called Img2Loc for geolocating precise locations from images. The authors reframe image geolocalization as a text generation task, using large multi-modality models like GPT4V or LLaVA with retrieval augmented generation. They employ CLIP-based representations to generate an image-based coordinate query database and then combine this with the images themselves to form customized prompts for the LMMs. The system is tested on benchmark datasets such as Im2GPS3k and YFCC4k, surpassing previous state-of-the-art models without any model training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Img2Loc is a new way to find exact locations in images using artificial intelligence. Instead of classifying images into grid cells or matching them with a database, this system uses large language models to generate text that can help locate the image on a map. The authors test it on two big datasets and show that it works better than other methods without needing to train the model. |
Keywords
» Artificial intelligence » Retrieval augmented generation » Text generation
