UniRain：基于 RAG 数据集蒸馏与多目标重加权优化的统一图像去雨
UniRain: Unified Image Deraining with RAG-based Dataset Distillation and Multi-objective Reweighted Optimization
把 RAG 检索与数据集蒸馏结合，从多个公开去雨数据集中筛选高质量样本做混合训练，并用非对称 MoE 加多目标重加权优化统一处理雨条、雨滴、白天与夜间退化，可作为统一复原模型的数据筛选与专家路由范式复用。
原文依据 an intelligent retrieval augmented generation (RAG)-based dataset distillation pipeline that selects high-quality training samples
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管图像去雨已取得显著进展，但我们注意到现有方法大多只针对特定类型的雨退化设计，难以泛化到多样的真实雨天场景。如何在统一框架内有效建模不同的雨退化，对真实场景图像去雨至关重要。本文提出 UniRain，一个有效的统一图像去雨框架，能够复原在白天与夜间条件下受雨条和雨滴退化的图像。为更好地提升统一模型的泛化能力，我们构建了一条基于智能检索增强生成（RAG）的数据集蒸馏流程，从所有公开去雨数据集中筛选高质量训练样本，以实现更好的混合训练。此外，我们将一种简单而有效的多目标重加权优化策略引入非对称混合专家（MoE）架构，以促进性能的一致性并提升跨多样场景的鲁棒性。大量实验表明，在我们提出的基准和多个公开数据集上，该框架优于最优模型。
英文原文摘要 1196 字符
Despite significant progress has been made in image deraining, we note that most existing methods are often developed for only specific types of rain degradation and fail to generalize across diverse real-world rainy scenes. How to effectively model different rain degradations within a universal framework is important for real-world image deraining. In this paper, we propose UniRain, an effective unified image deraining framework capable of restoring images degraded by rain streak and raindrop under both daytime and nighttime conditions. To better enhance unified model generalization, we construct an intelligent retrieval augmented generation (RAG)-based dataset distillation pipeline that selects high-quality training samples from all public deraining datasets for better mixed training. Furthermore, we incorporate a simple yet effective multi-objective reweighted optimization strategy into the asymmetric mixture-of-experts (MoE) architecture to facilitate consistent performance and improve robustness across diverse scenes. Extensive experiments show that our framework performs favorably against the state-of-the-art models on our proposed benchmarks and multiple public datasets.