未明确提及

本研究通过多模态域适应技术，创建了一个包含8694张图像的多样化水下环境数据集，旨在解决合成数据与真实水下环境之间的差异问题。该数据集通过将合成水下图像的内容代码与真实水下图像的风格代码结合，生成更接近真实水下条件的图像。此数据集适用于训练和测试水下图像颜色恢复模型，有助于提高水下视觉任务的性能，如海洋生物监测和海洋研究。

This study developed a diverse underwater environment dataset containing 8,694 images via multimodal domain adaptation techniques, aiming to address the discrepancy between synthetic data and real underwater environments. This dataset generates images that are closer to real underwater conditions by combining the content codes of synthetic underwater images and the style codes of real underwater images. This dataset is suitable for training and testing underwater image color restoration models, and helps improve the performance of underwater vision tasks such as marine organism monitoring and marine research.