Predict Before You Match: Cross-View Predictive Learning for Aerial-Ground Person Re-Identification

The Aerial-to-Ground Person Re-identification (AG-ReID) task requires identity matching across observation data with significant visual feature discrepancies. Existing methods primarily address these differences through view-invariant or view-aware representation learning. This paper explores a complementary approach: although the visual appearance of the same identity varies across views, predictable correlations persist between them. This implies that, beyond direct feature alignment, cross-view predictive relationships can serve as a latent supervisory signal for identity. Based on this insight, we propose an identity-conditioned cross-view latent prediction method, Predictive Re-identification across Views (PReView), designed to learn predictive relationships between observations of the same identity captured by different camera platforms. The method employs lightweight predictors to map global representations from a source view to region-level latent targets extracted from a target view. This bidirectional prediction mechanism is jointly optimized with the standard ReID objective, encouraging the encoder to learn representations that facilitate both identity discrimination and cross-view prediction. The predictors and target encoders are utilized solely during training, while the standard retrieval pipeline remains unchanged during inference. Our method demonstrates promising retrieval performance for aerial-ground person re-identification.