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Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization
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Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization
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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. 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.
Forward citations
Cited by 5 Pith papers
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VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale
VidTAG achieves fine-grained global video-to-GPS geolocalization via temporal frame alignment and denoising sequence refinement, reporting 20% gains at 1 km over GeoCLIP and 25% on CityGuessr68k.
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Skill-Conditioned Visual Geolocation for Vision-Language Models
GeoSkill uses an evolving Skill-Graph initialized from expert trajectories and grown via autonomous analysis of successful and failed reasoning rollouts to boost geolocation accuracy, faithfulness, and generalization ...
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Skill-Conditioned Visual Geolocation for Vision-Language Models
GeoSkill lets vision-language models improve geolocation accuracy and reasoning by maintaining an evolving Skill-Graph that grows through autonomous analysis of successful and failed rollouts on web-scale image data.
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MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding
MMLandmarks supplies 197k aerial and 329k ground images plus text and GPS for 18,557 landmarks to benchmark multimodal geo-spatial understanding.
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One Stone, Three Birds: Self-adaptive Optimal Transport for Multi-VLM Selection, Adaptation, and Ensembling
OSTB estimates a consensus sample-to-class transport plan from multiple frozen VLMs to perform model selection by reliability ranking, target adaptation via transport-conditioned classifiers, and ensembling via reliab...
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