  Pre-trained Vision-Language Models (VLMs) utilizing extensive image-text
paired data have demonstrated unprecedented image-text association
capabilities, achieving remarkable results across various downstream tasks. A
critical challenge is how to make use of existing large-scale pre-trained VLMs,
which are trained on common objects, to perform the domain-specific transfer
for accomplishing domain-related downstream tasks. A critical challenge is how
to make use of existing large-scale pre-trained VLMs, which are trained on
common objects, to perform the domain-specific transfer for accomplishing
domain-related downstream tasks. In this paper, we propose a new framework that
includes the Domain pre-trained Vision-Language Model (DVLM), bridging the gap
between the General Vision-Language Model (GVLM) and domain-specific downstream
tasks. Moreover, we present an image-text paired dataset in the field of remote
sensing (RS), RS5M, which has 5 million RS images with English descriptions.
The dataset is obtained from filtering publicly available image-text paired
datasets and captioning label-only RS datasets with pre-trained VLM. These
constitute the first large-scale RS image-text paired dataset. Additionally, we
fine-tuned the CLIP model and tried several Parameter-Efficient Fine-Tuning
methods on RS5M to implement the DVLM. Experimental results show that our
proposed dataset is highly effective for various tasks, and our model GeoRSCLIP
improves upon the baseline or previous state-of-the-art model by $3\%\sim20\%$
in Zero-shot Classification (ZSC), $3\%\sim6\%$ in Remote Sensing Cross-Modal
Text-Image Retrieval (RSCTIR) and $4\%\sim5\%$ in Semantic Localization (SeLo)
tasks. Dataset and models have been released in:
\url{https://github.com/om-ai-lab/RS5M}.
