Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information

Abstract

Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on remote sensing (RS) images. However, current RSCTIR methods mainly focus on global features of RS images, which leads to the neglect of local features that reflect target relationships and saliency. In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels. MIDF leverages local information to correct global information, utilizes global information to supplement local information, and uses the dynamic addition of the two to generate prominent visual representation. To alleviate the pressure of the redundant targets on the graph convolution network (GCN) and to improve the model s attention on salient instances during modeling local features, the de-noised representation matrix and the enhanced adjacency matrix (DREA) are devised to assist GCN in producing superior local representations. DREA not only filters out redundant features with high similarity, but also obtains more powerful local features by enhancing the features of prominent objects. Finally, to make full use of the information in the similarity matrix during inference, we come up with a plug-and-play multivariate rerank (MR) algorithm. The algorithm utilizes the k nearest neighbors of the retrieval results to perform a reverse search, and improves the performance by combining multiple components of bidirectional retrieval. Extensive experiments on public datasets strongly demonstrate the state-of-the-art performance of GaLR methods on the RSCTIR task. The code of GaLR method, MR algorithm, and corresponding files have been made available at https://github.com/xiaoyuan1996/GaLR .

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Cited by in corpus (8)

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• Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
• Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks
• Direction-Oriented Visual-semantic Embedding Model for Remote Sensing Image-text Retrieval
• Learning to Evaluate Performance of Multi-modal Semantic Localization
• Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning
• Cross-Modal Pre-Aligned Method with Global and Local Information for Remote-Sensing Image and Text Retrieval
• Multilingual Vision-Language Pre-training for the Remote Sensing Domain