SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment

TL;DR

SAGE is a novel vision-language framework designed for industrial anomaly detection, integrating fact enhancement and entropy-aware optimization to improve reasoning, interpretability, and generalization to unseen categories.

Contribution

The paper introduces SAGE, a VLM-based approach with fact enhancement and entropy-aware optimization, along with a new dataset and evaluation framework for industrial anomaly reasoning.

Findings

SAGE outperforms existing models on industrial anomaly datasets in zero-shot and one-shot settings.

The AD-PL dataset contains 28,415 expert-annotated question-answering instances for industrial anomaly reasoning.

Multiscale Logical Evaluation (MLE) effectively measures model logic and consistency in anomaly reasoning.

Abstract

While Vision-Language Models (VLMs) have shown promising progress in general multimodal tasks, they often struggle in industrial anomaly detection and reasoning, particularly in delivering interpretable explanations and generalizing to unseen categories. This limitation stems from the inherently domain-specific nature of anomaly detection, which hinders the applicability of existing VLMs in industrial scenarios that require precise, structured, and context-aware analysis. To address these challenges, we propose SAGE, a VLM-based framework that enhances anomaly reasoning through Self-Guided Fact Enhancement (SFE) and Entropy-aware Direct Preference Optimization (E-DPO). SFE integrates domain-specific knowledge into visual reasoning via fact extraction and fusion, while E-DPO aligns model outputs with expert preferences using entropy-aware optimization. Additionally, we introduce AD-PL, a preference-optimized dataset tailored for industrial anomaly reasoning, consisting of 28,415 question-answering instances with expert-ranked responses. To evaluate anomaly reasoning models, we develop Multiscale Logical Evaluation (MLE), a quantitative framework analyzing model logic and consistency. SAGE demonstrates superior performance on industrial anomaly datasets under zero-shot and one-shot settings. The code, model and dataset are available at https://github.com/amoreZgx1n/SAGE.