  The dominant CNN-based methods for cross-view image geo-localization rely on
polar transform and fail to model global correlation. We propose a pure
transformer-based approach (TransGeo) to address these limitations from a
different perspective. TransGeo takes full advantage of the strengths of
transformer related to global information modeling and explicit position
information encoding. We further leverage the flexibility of transformer input
and propose an attention-guided non-uniform cropping method, so that
uninformative image patches are removed with negligible drop on performance to
reduce computation cost. The saved computation can be reallocated to increase
resolution only for informative patches, resulting in performance improvement
with no additional computation cost. This "attend and zoom-in" strategy is
highly similar to human behavior when observing images. Remarkably, TransGeo
achieves state-of-the-art results on both urban and rural datasets, with
significantly less computation cost than CNN-based methods. It does not rely on
polar transform and infers faster than CNN-based methods. Code is available at
https://github.com/Jeff-Zilence/TransGeo2022.
