Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization

Understand

Image geolocalization is the challenging task of predicting the geographic coordinates of origin for a given photo.

• It is an unsolved problem relying on the ability to combine visual clues with general knowledge about the world to make accurate predictions across geographies.
• We present $\href{https://huggingface.co/geolocal/StreetCLIP}{\text{StreetCLIP}}$, a robust, publicly available foundation model not only achieving state-of-the-art performance on multiple open-domain image geolocalization benchmarks but also doing so in a zero-shot setting, outperforming supervised models trained on more than 4 million images.
• Our method introduces a meta-learning approach for generalized zero-shot learning by pretraining CLIP from synthetic captions, grounding CLIP in a domain of choice.