Bridging the Sky and Ground: Towards View-Invariant Feature Learning for Aerial-Ground Person Re-Identification

Aerial-Ground Person Re-Identification (AG-ReID) is a practical yet challenging task that involves cross-platform matching between aerial and ground cameras. Existing person Re-Identification (Re-ID) methods are primarily designed for homogeneous camera settings, such as groundto-ground or aerial-to-aerial matching. Therefore, these conventional Re-ID approaches underperform due to the significant viewpoint discrepancies introduced by crossplatform cameras in the AG-ReID task. To address this limitation, we propose a novel and efficient approach, termed View-Invariant Feature Learning for Aerial-Ground Person Re-Identification (VIF-AGReID), which explores viewinvariant features without leveraging any auxiliary information. Our approach introduces two key components:

(1) Patch-Level RotateMix (PLRM), an augmentation strategy that enhances rotational diversity within local regions of training samples, enabling the model to capture finegrained view-invariant features, and (2) View-Invariant Angular Loss (VIAL), which mitigates the impact of perspective variations by imposing angular constraints that exponentially penalize large angular deviations, optimizing the similarity of positive pairs while enhancing dissimilarity for hard negatives. These components interact synergistically to drive view-invariant feature learning, enhancing robustness across diverse viewpoints. Extensive experiments on the CARGO, AG-ReIDv1, and AG-ReIDv2 benchmarks demonstrate the effectiveness of our method in addressing the AG-ReID task.