标题： 自然语言中所有一体化视频修复的接地退化 显示英文标题

标题： Grounding Degradations in Natural Language for All-In-One Video Restoration

摘要： 在本工作中，我们提出了一种一体化视频恢复框架，该框架通过基础模型在自然语言中定位视频帧的退化感知语义上下文，提供可解释且灵活的指导。 与现有技术不同，我们的方法在训练或测试时假设没有退化知识，并学习对定位知识的近似，使得基础模型在推理过程中可以安全地解耦，且不增加额外成本。 此外，我们呼吁在一体化视频恢复中标准化基准，并提出了两种多退化设置下的基准，三任务（3D）和四任务（4D），以及两个时间变化的复合退化基准；其中后者是我们提出的具有不同雪强度的数据集，模拟天气退化如何自然地影响视频。 我们将我们的方法与现有工作进行了比较，并在所有基准上报告了最先进的性能。 显示英文摘要

摘要： In this work, we propose an all-in-one video restoration framework that grounds degradation-aware semantic context of video frames in natural language via foundation models, offering interpretable and flexible guidance. Unlike prior art, our method assumes no degradation knowledge in train or test time and learns an approximation to the grounded knowledge such that the foundation model can be safely disentangled during inference adding no extra cost. Further, we call for standardization of benchmarks in all-in-one video restoration, and propose two benchmarks in multi-degradation setting, three-task (3D) and four-task (4D), and two time-varying composite degradation benchmarks; one of the latter being our proposed dataset with varying snow intensity, simulating how weather degradations affect videos naturally. We compare our method with prior works and report state-of-the-art performance on all benchmarks.