QVAM: Query-guided View-aware Adaptive Modulation for Aerial-Ground Person Re-Identification

Aerial–Ground Person Re-Identification (AGPReID) matches identities across unmanned aerial vehicles (UAVs) and ground cameras but suffers from extreme viewpoint gaps. Most existing methods rely on coarse binary aerial/ground labels and rigid orthogonality to disentangle view and identity features. We identify an important limitation of prior methods: the oversimplified binary-label design cannot fully capture continuous aerial viewpoint shifts. Moreover, rigid orthogonality constraints may further suppress identity cues. To address these issues, we propose Query-guided View-aware Adaptive Modulation (QVAM) for identity-preserving cross-view matching. Specifically, a View-aware Decoder (VAD) distills fine-grained viewpoint cues from patch tokens using learnable view queries. Guided by these cues, Adaptive Feature Modulation (AFM) predicts query-conditioned masks to suppress view-biased responses while preserving identity discrimination. A Cross-View Prototype Alignment (CVPA) loss further aligns modulated features at batch and memory levels with dual-view memory banks. Extensive experiments on AG-ReID, AG-ReIDv2, and CARGO show that QVAM achieves stateof-the-art performance, improving the previous best results by +10.26% Rank-1/+10.83% mAP on CARGO-ALL and +2.62% Rank-1/+2.37% mAP on AG-ReID A→G. The code is available at https://github.com/ Sakuraandroxy/QVAM.