Urbanization challenges underscore the necessity for effective satellite
image-text retrieval methods to swiftly access specific information enriched
with geographic semantics for urban applications. However, existing methods
often overlook significant domain gaps across diverse urban landscapes,
primarily focusing on enhancing retrieval performance within single domains. To
tackle this issue, we present UrbanCross, a new framework for cross-domain
satellite image-text retrieval. UrbanCross leverages a high-quality,
cross-domain dataset enriched with extensive geo-tags from three countries to
highlight domain diversity. It employs the Large Multimodal Model (LMM) for
textual refinement and the Segment Anything Model (SAM) for visual
augmentation, achieving a fine-grained alignment of images, segments and texts,
yielding a 10% improvement in retrieval performance. Additionally, UrbanCross
incorporates an adaptive curriculum-based source sampler and a weighted
adversarial cross-domain fine-tuning module, progressively enhancing
adaptability across various domains. Extensive experiments confirm UrbanCross's
superior efficiency in retrieval and adaptation to new urban environments,
demonstrating an average performance increase of 15% over its version without
domain adaptation mechanisms, effectively bridging the domain gap.

UrbanCross 是一个新的框架，用于跨领域的卫星图像 - 文本检索，利用高质量的跨领域数据集和文本细化、图像增强等技术实现了图像、段落和文本的精细对齐，并通过自适应课程源采样和加权对抗跨领域微调等机制有效地解决了领域差异问题。在广泛实验证实 UrbanCross 在检索和适应新的城市环境方面的卓越性能，相比无领域适应机制的版本平均性能提高了 15%，成功地缩小了领域差距。