A Fast and Accurate Method for Remote Sensing Image-Text Retrieval Based On Large Model Knowledge Distillation

With the increasing development of remote sensing (RS) technology, remote sensing cross-modal image-text retrieval (RSCMITR) task has gradually attracted wide attention. At present, the large-scale pre-training model is brilliant in the field of natural images cross-modal retrieval, but the current RSCMITR models do not focus on it, resulting in less retrieval performance improvement. This paper proposes a lightweight network structure based on large-scale pre-training model and knowledge distillation, designing a lightweight model based on separable convolution and text convolution. Knowledge distillation technology is used to make the Light model learn the hidden knowledge of large-scale model CLIP-RS, which realizes fast and accurate retrieval. The proposed method achieves state-of-the-art performance on four commonly used RSCMITR datasets.