Summary of Urbancross: Enhancing Satellite Image-text Retrieval with Cross-domain Adaptation, by Siru Zhong et al.
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation
by Siru Zhong, Xixuan Hao, Yibo Yan, Ying Zhang, Yangqiu Song, Yuxuan Liang
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for cross-domain satellite image-text retrieval is proposed to bridge the domain gap between diverse urban landscapes. UrbanCross leverages a large-scale dataset with geo-tags from three countries and employs the Large Multimodal Model (LMM) and Segment Anything Model (SAM) to achieve fine-grained alignment of images, segments, and texts. The framework also incorporates an adaptive curriculum-based source sampler and weighted adversarial cross-domain fine-tuning module to enhance adaptability across domains. Experimental results demonstrate a 10% improvement in retrieval performance and a 15% increase in average performance when using domain adaptation mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to find specific information in satellite images that is related to geographic locations. This method, called UrbanCross, can work with different types of urban environments from around the world. It uses a big dataset with lots of information about these places and special computer models to connect the images with words that describe them. The result is a better way to find what you’re looking for in satellite images, which is important for planning and managing cities. |
Keywords
» Artificial intelligence » Alignment » Domain adaptation » Fine tuning » Sam
