Mutual Generative Transformer Learning for Cross-view Geo-localization
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Cross-view geo-localization (CVGL), which aims to estimate the geographical location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, remains extremely challenging due to the drastic appearance differences across views. Existing methods mainly employ Siamese-like CNNs to extract global descriptors without examining the mutual benefits between the two modes. In this paper, we present a novel approach using cross-modal knowledge generative tactics in combination with transformer, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, MGTL develops two separate generative modules–one for aerial-like knowledge generation from ground-level semantic information and vice versa–and fully exploits their mutual benefits through the attention mechanism. Experiments on challenging public benchmarks, CVACT and CVUSA, demonstrate the effectiveness of the proposed method compared to the existing state-of-the-art models.
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