VAGeo: View-specific Attention for Cross-View Object Geo-Localization

• URL: http://arxiv.org/abs/2501.07194v1
• Date: Mon, 13 Jan 2025 10:42:18 GMT
• Title: VAGeo: View-specific Attention for Cross-View Object Geo-Localization
• Authors: Zhongyang Li, Xin Yuan, Wei Liu, Xin Xu,
• Abstract summary: Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. This paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL.
• Score: 19.4845592498138
• License:
• Abstract: Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view.

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