An End-to-End Framework Based on Vision-Language Fusion for Remote Sensing Cross-Modal Text-Image Retrieval

Remote sensing cross-modal text-image retrieval (RSCTIR) has recently attracted extensive attention due to its advantages of fast extraction of remote sensing image information and flexible human-computer interaction. Traditional RSCTIR methods mainly focus on improving the performance of uni-modal feature extraction separately, and most rely on pre-trained object detectors to obtain better local feature representation, which not only lack multi-modal interaction information, but also cause the training gap between the pre-trained object detector and the retrieval task. In this paper, we propose an end-to-end RSCTIR framework based on vision-language fusion (EnVLF) consisting of two uni-modal (vision and language) encoders and a muti-modal encoder which can be optimized by multitask training. Specifically, to achieve an end-to-end training process, we introduce a vision transformer module for image local features instead of a pre-trained object detector. By semantic alignment of visual and text features, the vision transformer module achieves the same performance as pre-trained object detectors for image local features. In addition, the trained multi-modal encoder can improve the top-one and top-five ranking performances after retrieval processing. Experiments on common RSICD and RSITMD datasets demonstrate that our EnVLF can obtain state-of-the-art retrieval performance.