  Vision-and-language pre-training (VLP) models have experienced a surge in
popularity recently. By fine-tuning them on specific datasets, significant
performance improvements have been observed in various tasks. However, full
fine-tuning of VLP models not only consumes a significant amount of
computational resources but also has a significant environmental impact.
Moreover, as remote sensing (RS) data is constantly being updated, full
fine-tuning may not be practical for real-world applications. To address this
issue, in this work, we investigate the parameter-efficient transfer learning
(PETL) method to effectively and efficiently transfer visual-language knowledge
from the natural domain to the RS domain on the image-text retrieval task. To
this end, we make the following contributions. 1) We construct a novel and
sophisticated PETL framework for the RS image-text retrieval (RSITR) task,
which includes the pretrained CLIP model, a multimodal remote sensing adapter,
and a hybrid multi-modal contrastive (HMMC) learning objective; 2) To deal with
the problem of high intra-modal similarity in RS data, we design a simple yet
effective HMMC loss; 3) We provide comprehensive empirical studies for
PETL-based RS image-text retrieval. Our results demonstrate that the proposed
method is promising and of great potential for practical applications. 4) We
benchmark extensive state-of-the-art PETL methods on the RSITR task. Our
proposed model only contains 0.16M training parameters, which can achieve a
parameter reduction of 98.9% compared to full fine-tuning, resulting in
substantial savings in training costs. Our retrieval performance exceeds
traditional methods by 7-13% and achieves comparable or better performance than
full fine-tuning. This work can provide new ideas and useful insights for RS
vision-language tasks.
