Text-based Aerial-Ground Person Retrieval

This work introduces Text-based Aerial-Ground Person Retrieval (T AG-PR), which aims to retrieve person images from heterogeneous aerial and ground views with textual descriptions. Unlike traditional Text-based Person Retrieval (T -PR), which focuses solely on ground-view images, T AG-PR introduces greater practical significance and presents unique challenges due to the large viewpoint discrepancy across images. To support this task, we contribute: (1) T AG-PEDES dataset, constructed from public benchmarks with automatically generated textual descriptions, enhanced by a diversified text generation paradigm to ensure robustness under view heterogeneity; and (2) T AG-CLIP, a novel retrieval framework that addresses view heterogeneity through a hierarchically-routed mixture of experts module to learn view-specific and view-agnostic features and a viewpoint de-coupling strategy to decouple view-specific features for better cross-modal alignment. We evaluate the effectiveness of T AG-CLIP on both the proposed T AG-PEDES dataset and existing T -PR benchmarks. The dataset and code are available at https://github.com/Flame-Chasers/T