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

TL;DR

VAGeo introduces view-specific positional encoding and hybrid attention modules to improve cross-view object geo-localization accuracy by addressing viewpoint discrepancies and enhancing feature discrimination.

Contribution

The paper proposes a novel VAGeo method with view-specific positional encoding and hybrid attention modules for more accurate cross-view geo-localization.

Findings

Significant performance improvements on CVOGL dataset.

Increased accuracy at different thresholds for ground-view and drone-view images.

Effective handling of viewpoint discrepancies in object localization.

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.