VIGA: View-Conditioned and Identity-Guided Adaptation for Aerial-Ground Person Re-Identification

Aerial-ground person re-identification (AG-ReID) is signifi-cantly challenged by heterogeneous geometric regimes and severe foreground-background imbalance. Traditional shared-backbone architectures suf-fer from transformation conflicts when modeling disparate aerial andground views, while residual connections in transformers often lead toidentity embedding contamination from persistent background activa-tions. To address these issues, we propose View-conditioned and Identity-Guided Adaptation (VIGA), a unified framework that mitigates AG-ReID-specific aerial-ground discrepancies at both the parameter and ac-tivation levels. VIGA incorporates View-Conditioned Low-Rank Adap-tation to dynamically modulate feed-forward weights using view-awarelow-rank residuals, thereby alleviating view-dependent geometric map-ping conflicts without substantial parameter overhead. Simultaneously,we introduce Identity-Guided Sparsification, which employs a soft mask-ing mechanism guided by identity semantics to suppress irrelevant back-ground noise while preserving the 2D spatial topology. Extensive evalua-tions on the LAGPeR and AG-ReID.v2 datasets demonstrate that VIGAachieves state-of-the-art performance, particularly in challenging cross-platform retrieval scenarios, by effectively learning robust, view-invariantrepresentations.