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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 25·5 min read
What Does CLIP Learn for Regional Geolocalization? Probing Visual Cues and Scene Configuration After Adaptation
TL;DR
Pretrained CLIP features can significantly enhance regional geolocalization accuracy in urban environments, achieving up to 82.10% accuracy after adaptation. This study reveals the importance of scene configuration and visual cues in improving predictions.
✦ Why It Matters
Consider adapting CLIP models for improved accuracy in regional geolocalization tasks today.
Key Takeaways
- Utilize CLIP features for urban geolocalization tasks.
- Implement full fine-tuning for better accuracy and reduced prediction distance.
- Explore scene configuration sensitivity to enhance model performance.
- Recognize the importance of vegetation and sky in predictions.
Full Summary
Extracting detailed geographic information from street-view images is challenging due to similar visual cues in nearby areas. This study investigates the effectiveness of CLIP (Contrastive Language-Image Pretraining) features for regional geolocalization in Greater Los Angeles using 9,085 images.
Various adaptation methods were tested, including zero-shot CLIP, frozen-encoder readouts, and full fine-tuning. Results showed that while frozen methods maintained a 39.03% accuracy, adapted models achieved between 75.94% and 82.10%.
Full fine-tuning also reduced the average distance to the predicted region center from 12.30 km to 3.86 km. The study further examined the impact of visual cues through semantic cue removal and scene disruption, finding that adapted models were more sensitive to scene configuration.
However, adaptation did not enhance performance retention after appearance reduction, indicating that certain elements like vegetation and sky remain crucial.