用差异引导的强化学习，精准定位工业品细微缺陷。
DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models
- 将缺陷定位转为单次差异定位任务，提升跨场景泛化能力。
- 在13K对图像上实现显著优于基线的定位精度，媲美2350亿参数大模型。
- 适合工业质检中需识别未知缺陷类型的场景，尤其擅长处理细微异常。
工业缺陷定位旨在准确识别并定位工业产品中的异常区域，解决现实场景中检测未见缺陷类别这一关键挑战。传统闭集方法普遍存在跨场景泛化能力差的问题，而现有基于多模态大语言模型（MLLM）的方法存在两大局限：一是采用问答式范式，与实际定位需求不匹配；二是依赖标准优化技术如组相对策略优化（GRPO），难以为微小缺陷提供有效学习信号。为此，本文提出DifferAD-R1，一种面向工业缺陷定位的MLLM增强强化学习框架。设计差异引导的双图范式，将定位任务重构为单次差异定位问题，有效探索跨场景异常。构建双重一致性定位奖励机制，提升对难检测缺陷的优化稳定性和鲁棒性。此外，引入难度感知策略，结合自适应重加权与分组重采样，优先学习高难度样本。为支持真实工业场景评估，构建了包含20类、共13,000对图像的AD-DualDiff数据集。实验表明，DifferAD-R1显著优于现有基线，在性能上可与大型模型Qwen3-VL（235B参数）相媲美。代码已公开于：https://github.com/Rong2026/work-1。
原文摘要 · Abstract (English)
Industrial anomaly localization aims to accurately identify and localize abnormal regions in industrial products, addressing the critical challenge of detecting unseen defect categories in real-world scenarios. Traditional closed-set methods often suffer from poor cross-scenario generalization, while existingMultimodal Large Language Model (MLLM)-based approachesface two core limitations: they either adopt QA-style paradigmsmisaligned with the practical demands of localization, or relyon standard optimization techniques such as Group RelativePolicy Optimization (GRPO), which fails to deliver effectivelearning signals for subtle defects. To tackle these issues, thispaper proposes DifferAD-R1, an MLLM-augmented reinforcement learning framework tailored for industrial anomaly localization. We design a Difference-Guided dual-image paradigm,which reformulates the localization task as a one-shot difference grounding problem to effectively explore cross-scenarioanomalies. A Dual-Consistency Localization Reward is developedfor hard-to-detect anomalies, enhancing optimization stabilityand robustness. Additionally, we integrate a difficulty-awarestrategy with adaptive reweighting and group-wise resamplingto prioritize learning on challenging instances. To facilitateevaluations in real-world industrial settings, we construct theAD-DualDiff dataset, comprising 13K paired images across 20categories. Experimental results demonstrate that DifferADR1 significantly outperforms existing baselines and achievescompetitive performance compared to large-scale models likeQwen3-VL (235B parameters). Our code is publicly availableat: https://github.com/Rong2026/work-1.
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