General-purpose foundation models have led to recent breakthroughs in artificial
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …

Image captioning is a pretty modern area of the convergence of computer vision and natural
language processing and is widely used in a range of applications such as multi-modal …

Remote sensing (RS) cross-modal text-image retrieval has attracted extensive attention for
its advantages of flexible input and efficient query. However, traditional methods ignore the …

Because of the rapid growth of multimodal data from the internet and social media, a cross-
modal retrieval has become an important and valuable task in recent years. The purpose of …

Vision-and-language pretraining (VLP) models have experienced a surge in popularity
recently. By fine-tuning them on specific datasets, significant performance improvements …

The emerging field of vision–language models, which combines computer vision and natural
language processing (NLP), has gained significant interest and exploration. This integration …

Most of the remote sensing image captioning (IC) models are based on encoder–decoder
frameworks where a convolutional neural network (CNN) encodes the image information …

Change detection (CD) is among the most important applications in remote sensing (RS)
that allows identifying the changes that occurred in a given geographical area across …

Deep neural networks (DNNs) have been recently found popular for image captioning
problems in remote sensing (RS). Existing DNN-based approaches rely on the availability of …

Cross-modal text-image retrieval in remote sensing (RS) provides a flexible retrieval
experience for mining useful information from RS repositories. However, existing methods …