Around the World in 80 Timesteps: A Generative Approach to Global Visual
  Geolocation
Global visual geolocation predicts where an image was captured on Earth.
Since images vary in how precisely they can be localized, this task inherently
involves a significant degree of ambiguity. However, existing approaches are
deterministic and overlook this aspect. In this paper, we aim to close the gap
between traditional geolocalization and modern generative methods. We propose
the first generative geolocation approach based on diffusion and Riemannian
flow matching, where the denoising process operates directly on the Earth's
surface. Our model achieves state-of-the-art performance on three visual
geolocation benchmarks: OpenStreetView-5M, YFCC-100M, and iNat21. In addition,
we introduce the task of probabilistic visual geolocation, where the model
predicts a probability distribution over all possible locations instead of a
single point. We introduce new metrics and baselines for this task,
demonstrating the advantages of our diffusion-based approach. Codes and models
will be made available.
6758588398dbc91f46c532b5
Nicolas Dufour
claimed_verified
2024-12-10T16:12:23.515Z
630652803aed65d34e98eee3
https://cdn-avatars.huggingface.co/v1/production/uploads/630652803aed65d34e98eee3/XG_PuVFA6ziGQZd3UUZSF.jpeg
Nicolas Dufour
user
nicolas-dufour
6758588398dbc91f46c532b6
David Picard
admin_assigned
2024-12-10T16:17:26.420Z
6385b65fde54faafdea46f0b
/avatars/aba9aefa1dfa3d61ee3487b375a48ed5.svg
David
user
davidpicard
6758588398dbc91f46c532b7
Vicky Kalogeiton
claimed_verified
2024-12-11T09:20:40.980Z
6758941f1c12efeb74d88321
/avatars/01117d1c0195897f9ecfd2ff764252ea.svg
Vicky Kalogeiton
user
vkalogeiton
6758588398dbc91f46c532b8
Loic Landrieu
claimed_verified
2024-12-11T13:39:17.757Z
64f73dbe61afe1d3356b10cc
/avatars/bacf3c01515d06dc5f21f6f732f52b01.svg
landrieu
user
loicland
Around the World in 80 Timesteps: A Generative Approach to Global Visual
  Geolocation
Global visual geolocation predicts where an image was captured on Earth.
Since images vary in how precisely they can be localized, this task inherently
involves a significant degree of ambiguity. However, existing approaches are
deterministic and overlook this aspect. In this paper, we aim to close the gap
between traditional geolocalization and modern generative methods. We propose
the first generative geolocation approach based on diffusion and Riemannian
flow matching, where the denoising process operates directly on the Earth's
surface. Our model achieves state-of-the-art performance on three visual
geolocation benchmarks: OpenStreetView-5M, YFCC-100M, and iNat21. In addition,
we introduce the task of probabilistic visual geolocation, where the model
predicts a probability distribution over all possible locations instead of a
single point. We introduce new metrics and baselines for this task,
demonstrating the advantages of our diffusion-based approach. Codes and models
will be made available.
A generative geolocation approach using diffusion and Riemannian flow matching achieves state-of-the-art performance on visual geolocation tasks and introduces probabilistic geolocation with new metrics.
diffusion Riemannian flow matching denoising probabilistic visual geolocation visual geolocation benchmarks OpenStreetView-5M YFCC-100M iNat21