RAPR: Reliability-Adaptive Prompt Routing for Aerial-Ground Person Re-Identification

Aerial-ground person re-identification matches pedestrians across drone and ground cameras, whose radically different imaging geometries alter body-part visibility, scale, and context. Consequently, local cues can range from identity-bearing garment details to view-induced clutter and missing regions, making their reliability highly uneven. We present Reliability-Adaptive Prompt Routing (RAPR), a Transformer framework that uses reliability to guide prompt adaptation and local aggregation. RAPR derives a view-reduced identity anchor and dynamically initializes prompts from a shared prompt and an input-conditioned prompt bank. It then selects anchor-consistent local evidence and routes global, reliable-local, and residual view-rejection contexts to refine the prompts. A local refiner separately estimates token identity relevance and view bias to calibrate local memory and guide bidirectional prompt–local interaction. The final descriptor combines global and refined local features. RAPR outperforms multiple state-of-the-art methods across evaluation protocols on several AG-ReID benchmarks. Code and model weights will be released on GitHub upon acceptance.