Summary of Robust Disaster Assessment From Aerial Imagery Using Text-to-image Synthetic Data, by Tarun Kalluri et al.
Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data
by Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn, Sahil Singla, Manmohan Chandraker, Joseph Xu, Jeremiah Liu
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents a novel method to generate synthetic training data for damage assessment from aerial images using text-to-image generative models. The proposed approach leverages the capabilities of these models to create large-scale synthetic supervision for this task, addressing the challenge of poor robustness to domains where manual labeled data is unavailable. To achieve this, the authors develop a pipeline to efficiently generate thousands of post-disaster images from low-resource domains and propose a two-stage training approach to train robust models using both manual supervision and generated synthetic target domain data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research aims to improve our ability to assess damage in disaster situations where we don’t have enough labeled data. It does this by using computer models that can generate images based on text descriptions and then trains a special kind of AI model called a neural network using both real and generated image data. The authors tested their approach using aerial images from different regions and found it improved the accuracy of damage assessment compared to traditional methods. |
Keywords
» Artificial intelligence » Neural network
