Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization

Understand

Cross-view geo-localization aims to spot images of the same location shot from two platforms, e.g., the drone platform and the satellite platform.

• Existing methods usually focus on optimizing the distance between one embedding with others in the feature space, while neglecting the redundancy of the embedding itself.
• In this paper, we argue that the low redundancy is also of importance, which motivates the model to mine more diverse patterns.
• To verify this point, we introduce a simple yet effective regularization, i.e., Dynamic Weighted Decorrelation Regularization (DWDR), to explicitly encourage networks to learn independent embedding channels.