MoA-VR：一种面向全功能视频修复的多智能体系统
MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration
AI总结：
针对现实视频复杂退化，提出首个多智能体视频修复系统MoA-VR，通过退化识别、路由修复和质量评估三个智能体协同工作，在客观和感知质量上优于现有基线。
摘要：
现实世界中的视频常常因采集和传输条件多样而遭受复杂退化，例如噪声、压缩伪影和低光失真。现有的修复方法通常需要专业人员手动选择专用模型，或依赖无法在多种退化情况下泛化的单一架构。受专家经验启发，我们提出了MoA-VR，这是首个模仿人类专业人员推理和处理流程的Mixture-of-Agents视频修复系统，通过三个协调的智能体实现：退化识别、路由与修复，以及修复质量评估。具体而言，我们构建了一个大规模高分辨率视频退化识别基准，并构建了一个由视觉语言模型（VLM）驱动的退化识别器。我们进一步引入了一个由大语言模型（LLM）驱动的自适应路由器，该路由器通过观察工具使用模式自主学习有效的修复策略。为了评估中间和最终处理视频的质量，我们构建了Restored Video Quality（Res-VQ）数据集，并设计了一个专用于修复任务的基于VLM的视频质量评估（VQA）模型。大量实验表明，MoA-VR能有效处理多样化和复合退化，在客观指标和感知质量方面均持续优于现有基线。这些结果凸显了在通用视频修复系统中整合多模态智能和模块化推理的潜力。
英文摘要：
Real-world videos often suffer from complex degradations, such as noise, compression artifacts, and low-light distortions, due to diverse acquisition and transmission conditions. Existing restoration methods typically require professional manual selection of specialized models or rely on monolithic architectures that fail to generalize across varying degradations. Inspired by expert experience, we propose MoA-VR, the first \underline{M}ixture-\underline{o}f-\underline{A}gents \underline{V}ideo \underline{R}estoration system that mimics the reasoning and processing procedures of human professionals through three coordinated agents: Degradation Identification, Routing and Restoration, and Restoration Quality Assessment. Specifically, we construct a large-scale and high-resolution video degradation recognition benchmark and build a vision-language model (VLM) driven degradation identifier. We further introduce a self-adaptive router powered by large language models (LLMs), which autonomously learns effective restoration strategies by observing tool usage patterns. To assess intermediate and final processed video quality, we construct the \underline{Res}tored \underline{V}ideo \underline{Q}uality (Res-VQ) dataset and design a dedicated VLM-based video quality assessment (VQA) model tailored for restoration tasks. Extensive experiments demonstrate that MoA-VR effectively handles diverse and compound degradations, consistently outperforming existing baselines in terms of both objective metrics and perceptual quality. These results highlight the potential of integrating multimodal intelligence and modular reasoning in general-purpose video restoration systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
