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Article overview
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Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization | Lukas Haas
; Silas Alberti
; Michal Skreta
; | Date: |
1 Feb 2023 | Abstract: | 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
$href{this https URL}{ ext{StreetCLIP}}$, 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. We show that our method effectively
transfers CLIP’s generalized zero-shot capabilities to the domain of image
geolocalization, improving in-domain generalized zero-shot performance without
finetuning StreetCLIP on a fixed set of classes. | Source: | arXiv, 2302.00275 | Services: | Forum | Review | PDF | Favorites |
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