Summary of Enhanced Semantic Segmentation Pipeline For Weatherproof Dataset Challenge, by Nan Zhang et al.
Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset Challenge
by Nan Zhang, Xidan Zhang, Jianing Wei, Fangjun Wang, Zhiming Tan
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 winning solution to the WeatherProof Dataset Challenge (CVPR 2024 UG2+ Track 3) is an enhanced semantic segmentation pipeline that improves upon existing models. The approach involves using backbone pretrained with Depth Anything to enhance UperNet and SETRMLA models, adding language guidance based on weather and category information to InternImage model, and introducing a new dataset WeatherProofExtra with wider viewing angle and employing data augmentation methods, including adverse weather and super-resolution. Effective training strategies and ensemble method are applied to further improve performance. The solution is ranked 1st on the final leaderboard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Weather experts created a special dataset called WeatherProof to help machines understand weather better. They wanted to make computers better at identifying objects in different weather conditions, like rain or snow. To solve this problem, they came up with a new way to train computer models. This method uses information about the weather and what’s in the image to make the model more accurate. They even created extra images that are harder for the model to understand, so it can learn to be better at recognizing objects in different conditions. The team’s solution was the best one out of all the entries. |
Keywords
» Artificial intelligence » Data augmentation » Semantic segmentation » Super resolution
