Summary of Weatherproof: Leveraging Language Guidance For Semantic Segmentation in Adverse Weather, by Blake Gella and Howard Zhang and Rishi Upadhyay and Tiffany Chang and Nathan Wei and Matthew Waliman and Yunhao Ba and Celso De Melo and Alex Wong and Achuta Kadambi
WeatherProof: Leveraging Language Guidance for Semantic Segmentation in Adverse Weather
by Blake Gella, Howard Zhang, Rishi Upadhyay, Tiffany Chang, Nathan Wei, Matthew Waliman, Yunhao Ba, Celso de Melo, Alex Wong, Achuta Kadambi
First submitted to arxiv on: 21 Mar 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 method aims to infer semantic segmentation maps from images captured under adverse weather conditions, such as rain, fog, or snow. Existing models exhibit a significant performance drop when dealing with weather-degraded images, highlighting the need for robustness against various weather effects. The WeatherProof dataset is introduced, featuring accurate clear and adverse weather image pairs sharing an underlying scene, allowing analysis of error modes in existing models. To improve model performance, language guidance is used to identify the contributions of adverse weather conditions and inject that as “side information”. Models trained with this approach show notable gains in mIoU on WeatherProof, ACDC, and previous SOTA methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a way to help computers understand images even when they’re taken in bad weather. They found that current models struggle when dealing with pictures taken in the rain or snow, so they created a special dataset with pairs of clear and cloudy images. By analyzing how these models do on this new data, they figured out what’s going wrong and came up with a new way to train them using language as guidance. This approach helps computers perform better on weather-degraded images, which could be useful for tasks like self-driving cars or medical image analysis. |
Keywords
» Artificial intelligence » Semantic segmentation
