From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization

• URL: http://arxiv.org/abs/2503.07520v2
• Date: Mon, 11 Aug 2025 10:09:45 GMT
• Title: From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization
• Authors: Zhongwei Chen, Zhao-Xu Yang, Hai-Jun Rong, Jiawei Lang, Guoqi Li,
• Abstract summary: Traditional supervised drone-view geo-localization (DVGL) methods heavily depend on paired training data.<n>DVGL methods require obtaining the new paired data and subsequent retraining for model adaptation.<n>We propose a novel cross-domain invariant knowledge transfer network (CDIKTNet) with limited supervision.
• Score: 12.785100004522059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional supervised drone-view geo-localization (DVGL) methods heavily depend on paired training data and encounter difficulties in learning cross-view correlations from unpaired data. Moreover, when deployed in a new domain, these methods require obtaining the new paired data and subsequent retraining for model adaptation, which significantly increases computational overhead. Existing unsupervised methods have enabled to generate pseudo-labels based on cross-view similarity to infer the pairing relationships. However, geographical similarity and spatial continuity often cause visually analogous features at different geographical locations. The feature confusion compromises the reliability of pseudo-label generation, where incorrect pseudo-labels drive negative optimization. Given these challenges inherent in both supervised and unsupervised DVGL methods, we propose a novel cross-domain invariant knowledge transfer network (CDIKTNet) with limited supervision, whose architecture consists of a cross-domain invariance sub-network (CDIS) and a cross-domain transfer sub-network (CDTS). This architecture facilitates a closed-loop framework for invariance feature learning and knowledge transfer. The CDIS is designed to learn cross-view structural and spatial invariance from a small amount of paired data that serves as prior knowledge. It endows the shared feature space of unpaired data with similar implicit cross-view correlations at initialization, which alleviates feature confusion. Based on this, the CDTS employs dual-path contrastive learning to further optimize each subspace while preserving consistency in a shared feature space. Extensive experiments demonstrate that CDIKTNet achieves state-of-the-art performance under full supervision compared with those supervised methods, and further surpasses existing unsupervised methods in both few-shot and cross-domain initialization.

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