Dual Path Network for Cross-view Geo-Localization

Cross-view geo-localization is to find images of the same geographical target from different views. We reevaluate the square-ring partition strategy proposed in LPN and identify a limitation in its fixed splitting strategy of features. Specifically, we observe that this method struggles to accommodate the variations in target sizes in images. Consequently, the performance of LPN experiences a substantial decline when the dataset is expanded. To optimize this problem, we propose a multi-scale ring partition strategy that effectively partitions the features at different scales to accommodate the change in the target scale. Building upon this, we construct Dual Path Network(DPN). And, we investigate the distribution of training images in University-1652 and propose a new method of sampling training data, which improves Recall@1 by 4.5%. Experimental results on the University-1652 and University-160k datasets demonstrate the effectiveness of our method.