Category-Aware Global–Local Semantic Alignment for Remote Sensing Image–Text Retrieval

In remote sensing image–text retrieval (RSITR), precise cross-modal retrieval is often hindered by centroid drift and ambiguous decision boundaries caused by high inter-class visual similarity. To address these bottlenecks, this study proposes a Category-aware Global–Local Semantic Alignment (CGLSA) framework fine-tuned on the CLIP (ViT-B/16) backbone. The architecture orchestrates two complementary mechanisms: Global Semantic Collaborative Alignment that regularizes macro-level category centroids using momentum updates and Bayesian prior calibration, and Local Fine-grained Feature Alignment that refines instance-level matching via dynamic scale adjustment and category-aware topological masks. Extensive evaluations on three major benchmark datasets (RSICD, RSITMD, and UCM-Captions) validate the model’s efficacy. Compared to strictly controlled CLIP-family baselines under equivalent supervised conditions, CGLSA achieves new state-of-the-art performance across all R@K metrics and mean recall. Extensions adapting this robust centroid formation to semi-supervised and open-vocabulary scenarios are identified for future work.