From Limited Labels to Open Domains: An Efficient Learning Paradigm for UAV-view Geo-Localization

• URL: http://arxiv.org/abs/2503.07520v1
• Date: Mon, 10 Mar 2025 16:46:43 GMT
• Title: From Limited Labels to Open Domains: An Efficient Learning Paradigm for UAV-view Geo-Localization
• Authors: Zhongwei Chen, Zhao-Xu Yang, Hai-Jun Rong, Jiawei Lang,
• Abstract summary: Traditional UAV-view Geo-Localization (UVGL) supervised paradigms are constrained by the strict reliance on paired data for positive sample selection.<n>We propose a novel cross-domain invariance knowledge transfer network (CDIKTNet)<n>Our method achieves state-of-the-art performance under fully supervised conditions.
• Score: 2.502228766495945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional UAV-view Geo-Localization (UVGL) supervised paradigms are constrained by the strict reliance on paired data for positive sample selection, which limits their ability to learn cross-view domain-invariant representations from unpaired data. Moreover, it is necessary to reconstruct the pairing relationship with expensive re-labeling costs for scenario-specific training when deploying in a new domain, which fails to meet the practical demands of open-environment applications. To address this issue, we propose a novel cross-domain invariance knowledge transfer network (CDIKTNet), which comprises a cross-domain invariance sub-network and a cross-domain transfer sub-network to realize a closed-loop framework of invariance feature learning and knowledge transfer. The cross-domain invariance sub-network is utilized to construct an essentially shared feature space across domains by learning structural invariance and spatial invariance in cross-view features. Meanwhile, the cross-domain transfer sub-network uses these invariant features as anchors and employs a dual-path contrastive memory learning mechanism to mine latent cross-domain correlation patterns in unpaired data. Extensive experiments demonstrate that our method achieves state-of-the-art performance under fully supervised conditions. More importantly, with merely 2\% paired data, our method exhibits performance comparable to existing supervised paradigms and possesses the ability to transfer directly to qualify for applications in the other scenarios completely without any prior pairing relationship.

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