Semantic segmentation in rainy scenes is a challenging task due to the complex environment, class distribution imbalance, and limited annotated data. To address these challenges, we propose a novel framework that utilizes semi-supervised learning and pre-trained segmentation foundation model to achieve superior performance. Specifically, our framework leverages the semi-supervised model as the basis for generating raw semantic segmentation results, while also serving as a guiding force to prompt pre-trained foundation model to compensate for knowledge gaps with entropy-based anchors. In addition, to minimize the impact of irrelevant segmentation masks generated by the pre-trained foundation model, we also propose a mask filtering and fusion mechanism that optimizes raw semantic segmentation results based on the principle of minimum risk. The proposed framework achieves superior segmentation performance on the Rainy WCity dataset and is awarded the first prize in the sub-track of STRAIN in ICME 2023 Grand Challenges.

利用半监督学习和预训练的分割基础模型构建一个新的框架，以优化在雨季场景下进行的语义分割，利用熵锚点来指导预训练模型进行知识补全，并通过最小化风险的方式优化原始的语义分割结果，该框架在 Rainy WCity 数据集上实现了卓越的分割性能，获得了 ICME 2023 Grand Challenges 中 STRAIN 子赛道的第一名。

利用基于锚点提示的方法增强雨天场景中的分割