Triplet Interaction Mechanism in Cross-view Geo-localization

Cross-view geo-localization refers to inferring the geographical location from images of different viewpoints,which is usually viewed as an image retrieval task.However,most existing methods neglect the global position information and feature completeness,which makes the model can not conducive to capturing deep semantic information.Additionally,the current two-dimensional interaction methods do not fully utilize the relationships between dimensions,leading to insufficient cross-dimensional interaction.To address these issues,this paper designs a triplet interaction mechanism for cross-view geo-localization.This method uses ConvNeXt as the feature extraction network,followed by a proposed triplet interaction mechanism,for feature enrichment operations.Finally,a joint loss function is utilized to guide model training.It performs multiple dimensional interactions within the model,reducing the problem of information loss in the two-dimensional feature projection.The proposed method includes a triplet interaction mechanism that uses different attention mechanisms in three channels,making the model robust to translations,scaling,and rotations for different cross-view images.Experimental results demonstrate that the proposed method can significantly outperforms other methods for both drone view localization and drone navigation tasks on University-1652 dataset.