Bridging the Generalization Gap in Adverse Weather Segmentation: A Training Recipe Perspective

• URL: http://arxiv.org/abs/2605.27962v1
• Date: Wed, 27 May 2026 04:55:34 GMT
• Title: Bridging the Generalization Gap in Adverse Weather Segmentation: A Training Recipe Perspective
• Abstract summary: This paper describes our approach for the 8th UG2+ Workshop (CVPR 2026) Track2, which targets semantic segmentation of outdoor scenes degraded by five weather conditions.<n>A central challenge we observe is a severe generalization gap -- models that perform well on the validation set often collapse on the test set.<n>We investigate whether a carefully designed training recipe, rather than architectural complexity, can address this gap.
• Score: 9.751481477523956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes our approach for the 8th UG2+ Workshop (CVPR 2026) Track~2, which targets semantic segmentation of outdoor scenes degraded by five weather conditions: blur, darkness, snow, haze, and glare. A central challenge we observe is a severe generalization gap -- models that perform well on the validation set often collapse on the test set. For instance, SegFormer-B5 drops 16.1 mIoU points from validation to test, suggesting that model capacity alone is insufficient for robustness. We investigate whether a carefully designed training recipe, rather than architectural complexity, can address this gap. Starting from a pre-trained SegMAN-S backbone, we systematically study the effects of domain-adaptive fine-tuning, multi-source data mixing, scene-balanced sampling, and synthetic degradation augmentation. Our final system achieves 59.9\% mIoU on the official test set while maintaining a validation-test gap of only 6.5 points -- less than half that of larger models. We analyze negative results from architectural modifications, loss function variants, and model scaling to provide practical insights for weather-robust segmentation under limited data.

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