arXiv AI
What Does CLIP Learn for Regional Geolocalization? Probing Visual Cues and Scene Configuration After Adaptation
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OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization
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SLED: Scalable Location Encoding via Distillation
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Rethinking Pre-Training and Augmentation for Zero-Shot Cross-City Object Detection
The paper proposes a modular training pipeline for zero‑shot cross‑city object detection that combines a multi‑dataset pre‑training strategy with class‑agnostic objectness distillation and a domain‑resilient augmentation stream featuring a Grayworld transformation. Applied to the RF‑DETR detector, the approach reduces cross‑city distribution gaps while using only 16 GB GPU memory, achieving a 24.29‑point mAP improvement and 1st place on the AI City Challenge Track 6 leaderboard. The authors provide code and data at the referenced GitHub repository.