UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation
Siru Zhong, Xixuan Hao, Yibo Yan, Ying Zhang, Yangqiu Song, Yuxuan, Liang

TL;DR
UrbanCross introduces a cross-domain satellite image-text retrieval framework that leverages multimodal models and adaptive training to improve retrieval accuracy across diverse urban landscapes, addressing domain gap challenges.
Contribution
The paper presents UrbanCross, a novel framework combining multimodal models and adaptive training for effective cross-domain satellite image-text retrieval in urban environments.
Findings
Achieves 10% improvement in retrieval performance.
Demonstrates 15% average performance increase with domain adaptation.
Effectively bridges domain gaps across diverse urban landscapes.
Abstract
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,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Retrieval and Classification Techniques · Geographic Information Systems Studies
