  In this work, we aim at an important but less explored problem of a simple
yet effective backbone specific for cross-view geo-localization task. Existing
methods for cross-view geo-localization tasks are frequently characterized by
1) complicated methodologies, 2) GPU-consuming computations, and 3) a stringent
assumption that aerial and ground images are centrally or orientation aligned.
To address the above three challenges for cross-view image matching, we propose
a new backbone network, named Simple Attention-based Image Geo-localization
network (SAIG). The proposed SAIG effectively represents long-range
interactions among patches as well as cross-view correspondence with multi-head
self-attention layers. The "narrow-deep" architecture of our SAIG improves the
feature richness without degradation in performance, while its shallow and
effective convolutional stem preserves the locality, eliminating the loss of
patchify boundary information. Our SAIG achieves state-of-the-art results on
cross-view geo-localization, while being far simpler than previous works.
Furthermore, with only 15.9% of the model parameters and half of the output
dimension compared to the state-of-the-art, the SAIG adapts well across
multiple cross-view datasets without employing any well-designed feature
aggregation modules or feature alignment algorithms. In addition, our SAIG
attains competitive scores on image retrieval benchmarks, further demonstrating
its generalizability. As a backbone network, our SAIG is both easy to follow
and computationally lightweight, which is meaningful in practical scenario.
Moreover, we propose a simple Spatial-Mixed feature aggregation moDule (SMD)
that can mix and project spatial information into a low-dimensional space to
generate feature descriptors... (The code is available at
https://github.com/yanghongji2007/SAIG)
