  Existing person re-identification methods have achieved remarkable advances
in appearance-based identity association across homogeneous cameras, such as
ground-ground matching. However, as a more practical scenario, aerial-ground
person re-identification (AGPReID) among heterogeneous cameras has received
minimal attention. To alleviate the disruption of discriminative identity
representation by dramatic view discrepancy as the most significant challenge
in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet
effective framework. Two major components are designed in VDT to decouple
view-related and view-unrelated features, namely hierarchical subtractive
separation and orthogonal loss, where the former separates these two features
inside the VDT, and the latter constrains these two to be independent. In
addition, we contribute a large-scale AGPReID dataset called CARGO, consisting
of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images.
Experiments on two datasets show that VDT is a feasible and effective solution
for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on
CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational
complexity. Our project is available at https://github.com/LinlyAC/VDT-AGPReID
