Fig. 1
From: Enhancing cross view geo localization through global local quadrant interaction network
An illustration of the motivation behind our work. Our proposed GLQINet generates diverse patterns to encourage the network to learn informative feature representations by focusing on discriminative aspects of the input. In addition, the model employs an attention-based mechanism in an interactive manner to effectively learn both global and local features, enabling a comprehensive understanding of the geographic context across different views. The satellite imagery shown in this figure is derived from the University-1652 dataset 13, and the dataset can be accessed at: https://github.com/layumi/University1652-Baseline.
