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

Bridging the Generalization Gap in Adverse Weather Segmentation: A Training Recipe Perspective arXiv:2605.27962v1 Announce Type: new 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. F