Summary of Solution For Cvpr 2024 Ug2+ Challenge Track on All Weather Semantic Segmentation, by Jun Yu et al.
Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation
by Jun Yu, Yunxiang Zhang, Fengzhao Sun, Leilei Wang, Renjie Lu
First submitted to arxiv on: 9 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 This paper presents a novel approach to semantic segmentation in adverse weather conditions, specifically addressing the UG2+ Challenge at CVPR 2024. The authors initialize the InternImage-H backbone with pre-trained weights from a large-scale joint dataset and enhance it with the state-of-the-art Upernet segmentation method. To improve performance, they employ offline and online data augmentation approaches to extend the training set. As a result, their proposed solution demonstrates advanced performance on the test set and achieves 3rd position in this challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make computers better at recognizing things in pictures when it’s raining or snowing outside. The researchers use a special way of connecting different pieces of information together called Upernet to help the computer recognize what’s in the picture. They also use lots of training data and special tricks to make sure their computer is good at recognizing things even when the weather is bad. This makes it really useful for things like self-driving cars or drones. |
Keywords
» Artificial intelligence » Data augmentation » Semantic segmentation
