无需外部数据，用半监督方法提升恶劣天气下的语义分割精度
A Robust Semantic Segmentation Pipeline for the CVPR 2026 8th UG2+ Challenge Track 2
- 以UniMatch V2为基础，将恶劣天气图像视为无标签数据进行半监督训练
- 在WeatherProof数据集上达到81.3%的mIoU，显著优于基线模型
- 适合关注真实场景鲁棒性与零外部数据依赖的视觉算法研究者
本文针对CVPR 2026第八届UG2+挑战赛第二赛道——恶劣天气下的语义分割任务，提出一种半监督分割流水线。方法仅使用WeatherProof数据集训练，不依赖任何外部数据。采用UniMatch V2作为基线模型，将所有受退化影响的天气图像视为无标签数据，实现对挑战赛所提供数据分布的充分挖掘。推理阶段进一步引入测试时增强（test-time augmentation），有效提升预测结果的鲁棒性与分割精度。代码已开源：https://github.com/ylb888/weatherproof-challenge-unimatchv2。
原文摘要 · Abstract (English)
This report presents our solution for the WeatherProof Dataset Challenge, namely CVPR 2026 8th UG2+ Challenge Track 2: Semantic Segmentation in Adverse Weather. For the semantic segmentation task under adverse weather conditions, we propose a semi-supervised segmentation pipeline. Our method is trained exclusively on the WeatherProof dataset, without using any additional external data. Specifically, we adopt UniMatch V2 as the baseline model and treat all degraded-weather images as unlabeled data for semi-supervised training, thereby fully exploiting the data distribution provided by the challenge. During inference, we further apply test-time augmentation to improve the robustness and segmentation accuracy of the final predictions. The code is publicly available at: https://github.com/ylb888/weatherproof-challenge-unimatchv2.
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