Hierarchical Hyperbolic Representation Learning for Aerial-Ground Person Re-Identification

Aerial-Ground Person Re-IDentification (AG-ReID) aims toretrieve the same person across heterogeneous aerial and ground cameraplatforms. Although great progress, existing methods remain subopti-mal due to the direct feature alignment across views, overlooking view-specific cues. To address this issue, we propose a novel Hierarchical Hy-perbolic Representation (HiHR) framework for AG-ReID. More specif-ically, we first extract multi-granularity features based on pre-trainedvisual-text encoders. Then, we propose a Text-guided Multi-granularityFusion (TMF) to fuse multi-granularity features and enhance the repre-sentation ability of identity features. Furthermore, we introduce the Hi-erarchical Hyperbolic Learning (HHL) to construct a hierarchical featurestructure in a hyperbolic space. This hierarchy includes a coarse level thatensures identity separability and cross-view consistency, and a fine levelthat preserves view-specific discriminative cues. A a result, our proposedframework can effectively aggregate view-invariant and view-specific dis-criminative features for AG-ReID. Extensive experiments on four AG-ReID benchmarks demonstrate the effectiveness of our framework. Thesource code is available at https://github.com/YangQiWei3/HiHR.