Summary of Weatherproof: a Paired-dataset Approach to Semantic Segmentation in Adverse Weather, by Blake Gella et al.
WeatherProof: A Paired-Dataset Approach to Semantic Segmentation in Adverse Weather
by Blake Gella, Howard Zhang, Rishi Upadhyay, Tiffany Chang, Matthew Waliman, Yunhao Ba, Alex Wong, Achuta Kadambi
First submitted to arxiv on: 15 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 introduction of large, foundational models to computer vision has led to improved performance on semantic segmentation tasks. However, these methods suffer a significant drop in performance when testing on images degraded by weather conditions like rain, fog, or snow. A new general paired-training method is introduced that can be applied to all current foundational model architectures, leading to better performance on adverse weather condition images. This is achieved through the creation of the WeatherProof Dataset, which includes accurate clear and adverse weather image pairs. Training models on these paired frames results in improved performance on adverse weather data by up to 18.4% compared to standard training procedures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Weather-proofing computer vision! Researchers created a new way to train AI models so they can better see through rain, fog, or snow. They made a special dataset with clear and cloudy images that helps the model learn to do its job better in tricky weather. This means we might get more accurate results from self-driving cars, surveillance cameras, and other computer vision applications on rainy or snowy days. |
Keywords
* Artificial intelligence * Semantic segmentation
