Chaoran Li

Image-based ground-to-aerial geo-localization aims to determine the geo-location of a ground query image by matching it with a large geo-tagged aerial image database. Due to the drastic difference between ground and aerial views, achieving high-accuracy geo-localization remains a huge challenge, especially in practical scenarios where ground query images have unknown orientations and even limited field-of-views (FoVs). Incomplete information significantly hampers the process of learning discriminative features for image matching. In this paper, we propose a novel AutoMatic Progressive LEarning method (AMPLE) for the orientation unknown geo-localization task. Specifically, we design a ConvNeXt-based network to effectively extract orientation-aware features from the two views. We then present two progressive training strategies without manually predefined training stages to promote the learning process. The first adaptively mines harder negative samples that contribute more to the loss, by automatically discarding redundant samples as the current best accuracy increases. The second leverages the proposed alignment-correlation hybrid loss to guide model optimization in a progressive manner, gradually reducing the reliance on auxiliary orientation information. Extensive experiments on two benchmark datasets demonstrate that AMPLE outperforms state-of-the-art methods in orientation unknown, FoV limited, and cross-area tasks. Lastly, we propose the concept of unifying unknown orientation tasks at different FoVs, and show the cross-FoV generalization capability of our method.