  Geolocation is a fundamental component of route planning and navigation for
unmanned vehicles, but GNSS-based geolocation fails under denial-of-service
conditions. Cross-view geo-localization (CVGL), which aims to estimate the
geographical location of the ground-level camera by matching against enormous
geo-tagged aerial (\emph{e.g.}, satellite) images, has received lots of
attention but remains extremely challenging due to the drastic appearance
differences across aerial-ground views. In existing methods, global
representations of different views are extracted primarily using Siamese-like
architectures, but their interactive benefits are seldom taken into account. In
this paper, we present a novel approach using cross-view knowledge generative
techniques in combination with transformers, namely mutual generative
transformer learning (MGTL), for CVGL. Specifically, by taking the initial
representations produced by the backbone network, MGTL develops two separate
generative sub-modules -- one for aerial-aware knowledge generation from
ground-view semantics and vice versa -- and fully exploits the entirely mutual
benefits through the attention mechanism. Moreover, to better capture the
co-visual relationships between aerial and ground views, we introduce a
cascaded attention masking algorithm to further boost accuracy. Extensive
experiments on challenging public benchmarks, \emph{i.e.}, {CVACT} and {CVUSA},
demonstrate the effectiveness of the proposed method which sets new records
compared with the existing state-of-the-art models.
