SARA: Structure-Aware Riemannian-Guided Alignment for Drone Image-Text Retrieval

Drone image–text retrieval (DITR) aims to associate aerialimages with textual descriptions in complex low-altitude environments.However, intricate spatial layouts and long-range structural dependen-cies in drone imagery challenge conventional Euclidean embedding mod-els, often leading to cross-modal misalignment. To address this issue, wepropose SARA, a structure-aware Riemannian-guided alignment frame-work that incorporates Riemannian manifold modeling to complementsemantic embeddings. Specifically, the Manifold Structural Feature Ex-traction (MSFE) module captures second-order structural dependenciesby encoding local feature correlations as symmetric positive definite rep-resentations. The Log-Euclidean Structural Alignment (LESA) moduleperforms geometry-consistent alignment in the tangent space, integratingsemantic correspondence with structural coherence through distributionalignment and feature fusion. We further provide theoretical analysisdemonstrating convergence and minimization of an upper bound on theexpected alignment error. Experiments on ERA and UDV benchmarksdemonstrate that SARA consistently improves retrieval performance overstate-of-the-art methods, particularly in structurally complex or seman-tically ambiguous drone scenarios.