Summary of Towards a Multimodal Framework For Remote Sensing Image Change Retrieval and Captioning, by Roger Ferrod and Luigi Di Caro and Dino Ienco
Towards a multimodal framework for remote sensing image change retrieval and captioning
by Roger Ferrod, Luigi Di Caro, Dino Ienco
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed foundation model for bi-temporal RS image pairs leverages Contrastive Learning and the LEVIR-CC dataset to enable change detection analysis, captioning, and text-image retrieval. This novel approach addresses the gap in multimodal applications involving remote sensing (RS) data, which typically focus on specific tasks like classification, captioning, and retrieval. The model jointly trains a contrastive encoder and captioning decoder to maintain comparable performance to state-of-the-art captioning models while adding text-image retrieval capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to use remote sensing (RS) data, which is useful for monitoring changes in the environment over time. The method combines image processing with natural language processing to detect changes and provide descriptions of what’s happening. This could be helpful for things like disaster response or land planning. |
Keywords
» Artificial intelligence » Classification » Decoder » Encoder » Natural language processing
