Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding
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.
6948b67134f46eaf46cbb2cc
Jiaqi Tang
claimed_verified
2025-12-22T10:58:32.085Z
642e7a12ccdcf5da7f9657a0
https://cdn-avatars.huggingface.co/v1/production/uploads/642e7a12ccdcf5da7f9657a0/w8jW5BagTuTp6EvC6KEyR.png
Jiaqi Tang
Jiaqi-hkust
user
Jiaqi-hkust
6948b67134f46eaf46cbb2cd
Jianmin Chen
6948b67134f46eaf46cbb2ce
Wei Wei
6948b67134f46eaf46cbb2cf
Xiaogang Xu
6948b67134f46eaf46cbb2d0
Runtao Liu
6948b67134f46eaf46cbb2d1
Xiangyu Wu
6948b67134f46eaf46cbb2d2
Qipeng Xie
6948b67134f46eaf46cbb2d3
Jiafei Wu
6948b67134f46eaf46cbb2d4
Lei Zhang
6948b67134f46eaf46cbb2d5
Qifeng Chen
Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding
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.
A novel framework, Robust-R1, enhances multimodal large language models' robustness to visual degradations through explicit modeling, supervised fine-tuning, reward-driven alignment, and dynamic reasoning depth scaling, achieving state-of-the-art performance on real-world degradation benchmarks.
multimodal large language models visual degradations visual encoder generalization supervised fine-tuning reward-driven alignment dynamic reasoning depth scaling R-Bench MMMB MMStar RealWorldQA