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GeoMFD: Continual Drone-View Geo-Localization with Geometry-Aware Adapter and Margin-Field Distillation
Zhongwei Chen, Hai-jun Rong, Tao Zhang +4
Existing drone-view geo-localization (DVGL) methods are mainly developed under a static training paradigm, where models are optimized for fixed environments with all training data…
Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localization
Zhongwei Chen, Hai-Jun Rong, Zhao-Xu Yang +1
Traditional drone-view geo-localization (DVGL) methods based on artificial neural networks (ANNs) have achieved remarkable performance. However, ANNs rely on dense computation, whi…
OT-Drive: Out-of-Distribution Off-Road Traversable Area Segmentation via Optimal Transport
Zhihua Zhao, Guoqiang Li, Chen Min +1
Reliable traversable area segmentation in unstructured environments is critical for planning and decision-making in autonomous driving. However, existing data-driven approaches oft…
From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization
Zhongwei Chen, Zhao-Xu Yang, Hai-Jun Rong +2
Traditional supervised drone-view geo-localization (DVGL) methods heavily depend on paired training data and encounter difficulties in learning cross-view correlations from unpaire…
Without Paired Labeled Data: End-to-End Self-Supervised Learning for Drone-view Geo-Localization
Zhongwei Chen, Zhao-Xu Yang, Hai-Jun Rong +1
Drone-view Geo-Localization (DVGL) aims to achieve accurate localization of drones by retrieving the most relevant GPS-tagged satellite images. However, most existing methods heavi…