通过约束分解和指令微调实现可解释的逻辑异常分类
Interpretable Logical Anomaly Classification via Constraint Decomposition and Instruction Fine-Tuning
AI总结：
LogiCls通过约束分解和指令微调实现工业逻辑异常的可解释分类，结合视觉-语言框架和数据驱动的监督方法，提升异常检测的准确性和可解释性。
摘要：
逻辑异常是指违反工业图像中预定义的物体数量、空间布局和组成关系的约束。尽管先前的工作大多将异常检测视为二元决策，此类方法无法指示哪个逻辑规则被违反，因此对质量保证的价值有限。我们引入了逻辑异常分类（LAC）任务，该任务在单步推理中统一了异常检测和细粒度违反分类。为解决LAC，我们提出LogiCls，一种视觉-语言框架，将复杂的逻辑约束分解为一系列可验证的子查询。我们进一步提出一种以数据为中心的指令合成管道，为这些子查询生成链式思维（CoT）监督，结合精确的地面真实标注与多样的图像-文本增强，以适应视觉语言模型（VLMs）进行逻辑敏感推理。通过难度感知的重采样策略稳定训练，该策略强调具有挑战性的子查询和长尾约束类型。大量实验表明，LogiCls能够提供稳健、可解释且准确的工业逻辑异常分类，不仅预测了违规类别，还提供了其证据轨迹。
英文摘要：
Logical anomalies are violations of predefined constraints on object quantity, spatial layout, and compositional relationships in industrial images. While prior work largely treats anomaly detection as a binary decision, such formulations cannot indicate which logical rule is broken and therefore offer limited value for quality assurance. We introduce Logical Anomaly Classification (LAC), a task that unifies anomaly detection and fine-grained violation classification in a single inference step. To tackle LAC, we propose LogiCls, a vision-language framework that decomposes complex logical constraints into a sequence of verifiable subqueries. We further present a data-centric instruction synthesis pipeline that generates chain-of-thought (CoT) supervision for these subqueries, coupling precise grounding annotations with diverse image-text augmentations to adapt vision language models (VLMs) to logic-sensitive reasoning. Training is stabilized by a difficulty-aware resampling strategy that emphasizes challenging subqueries and long tail constraint types. Extensive experiments demonstrate that LogiCls delivers robust, interpretable, and accurate industrial logical anomaly classification, providing both the predicted violation categories and their evidence trails.
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。
