Cross-Modal Pre-Aligned Method with Global and Local Information for Remote-Sensing Image and Text Retrieval
This work addresses retrieval challenges for remote sensing applications, offering incremental improvements in performance.
The paper tackled the problem of improving retrieval accuracy and efficiency in remote sensing cross-modal text-image retrieval by integrating global and local information and pre-aligning features, achieving up to 4.65% improvement in R@1 and 2.28% in mean Recall over state-of-the-art methods.
Remote sensing cross-modal text-image retrieval (RSCTIR) has gained attention for its utility in information mining. However, challenges remain in effectively integrating global and local information due to variations in remote sensing imagery and ensuring proper feature pre-alignment before modal fusion, which affects retrieval accuracy and efficiency. To address these issues, we propose CMPAGL, a cross-modal pre-aligned method leveraging global and local information. Our Gswin transformer block combines local window self-attention and global-local window cross-attention to capture multi-scale features. A pre-alignment mechanism simplifies modal fusion training, improving retrieval performance. Additionally, we introduce a similarity matrix reweighting (SMR) algorithm for reranking, and enhance the triplet loss function with an intra-class distance term to optimize feature learning. Experiments on four datasets, including RSICD and RSITMD, validate CMPAGL's effectiveness, achieving up to 4.65% improvement in R@1 and 2.28% in mean Recall (mR) over state-of-the-art methods.