Around the world in 80 steps
Plonk model will predict location based on picture
Paper: Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation
Code: https://nicolas-dufour.github.io/plonk
Researchers from Ecole des Ponts and Ecole Polytechnique are interested in geolocation models.
Hmm..What’s the background?
Global visual geolocation, the task of inferring the location of an image from its visual content, is important for various applications, including cultural heritage, forensics, and multimedia retrieval. However, traditional methods often struggle with the inherent ambiguity in geolocating images.
So what is proposed in the research paper?
Here are the main insights:
The researchers propose a novel generative approach to geolocation based on diffusion models and Riemannian flow matching on the Earth's surface. This approach generates trajectories onto the Earth, with the endpoint providing a location estimate
This generative approach outperforms state-of-the-art geolocation methods on several large-scale datasets, demonstrating its ability to handle ambiguous visual cues
The researchers introduce the task of probabilistic visual geolocation, where the model predicts a probability distribution over all possible locations. This provides a more nuanced understanding of the location information present in an image
What’s next?
While the generative approach shows great promise, retrieval-based techniques with large image databases still have an advantage at very fine-grained resolutions. Further research could focus on combining these approaches to leverage their respective strengths.
Plonk model will predict location based on picture
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