Robust-R1: 为鲁棒视觉理解的退化感知推理
Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding
AI总结：
Robust-R1通过结构化推理链显式建模视觉退化，提升多模态大语言模型在现实世界退化下的鲁棒性与抗干扰能力。
摘要：
多模态大语言模型在极端真实世界视觉退化下难以保持可靠性能，这阻碍了其在实际中的鲁棒性。现有鲁棒MLLMs主要依赖隐式训练/适应，仅关注视觉编码器泛化，导致可解释性有限和孤立优化。为克服这些限制，我们提出了Robust-R1，一种新的框架，通过结构化推理链显式建模视觉退化。我们的方法整合：(i) 有监督微调用于退化感知推理基础，(ii) 奖励驱动对齐用于准确感知退化参数，(iii) 动态推理深度缩放适应退化强度。为了促进这一方法，我们引入了一个专门的11K数据集，其中包含在四个关键真实世界视觉处理阶段合成的逼真退化，每个都标注有连接退化参数、感知影响、原始语义推理链和结论的结构化链。全面评估显示了最先进的鲁棒性：Robust-R1在现实世界退化基准R-Bench上优于所有通用和鲁棒基线，同时在MMMB、MMStar和RealWorldQA上保持优越的抗退化性能。
英文摘要：
Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust MLLMs predominantly rely on implicit training/adaptation that focuses solely on visual encoder generalization, suffering from limited interpretability and isolated optimization. To overcome these limitations, we propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains. Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity. To facilitate this approach, we introduce a specialized 11K dataset featuring realistic degradations synthesized across four critical real-world visual processing stages, each annotated with structured chains connecting degradation parameters, perceptual influence, pristine semantic reasoning chain, and conclusion. Comprehensive evaluations demonstrate state-of-the-art robustness: Robust-R1 outperforms all general and robust baselines on the real-world degradation benchmark R-Bench, while maintaining superior anti-degradation performance under multi-intensity adversarial degradations on MMMB, MMStar, and RealWorldQA.
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。
