# Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation

arXiv: https://arxiv.org/abs/2406.05837 · HTML: https://mlsanity.com/paper/2406.05837/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 20 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations](https://mlsanity.com/paper/1809.04766/) (2018)
- [Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs](https://mlsanity.com/paper/1802.08960/) (2018)
- [RTSeg: Real-time Semantic Segmentation Comparative Study](https://mlsanity.com/paper/1803.02758/) (2018)
- [WeatherProof: A Paired-Dataset Approach to Semantic Segmentation in Adverse Weather](https://mlsanity.com/paper/2312.09534/) (2023)
- [Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach](https://mlsanity.com/paper/2210.05626/) (2022)

## Abstract (arXiv)

In this report, we present our solution for the semantic segmentation in adverse weather, in UG2+ Challenge at CVPR 2024. To achieve robust and accurate segmentation results across various weather conditions, we initialize the InternImage-H backbone with pre-trained weights from the large-scale joint dataset and enhance it with the state-of-the-art Upernet segmentation method. Specifically, we utilize offline and online data augmentation approaches to extend the train set, which helps us to further improve the performance of the segmenter. As a result, our proposed solution demonstrates advanced performance on the test set and achieves 3rd position in this challenge.