A 3B multimodal model fine-tuned with supervised learning and GRPO achieves accuracy and F1 comparable to a 72B model on logical anomaly detection while generating structured textual explanations.
LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction
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abstract
Logical image understanding involves interpreting and reasoning about the relationships and consistency within an image's visual content. This capability is essential in applications such as industrial inspection, where logical anomaly detection is critical for maintaining high-quality standards and minimizing costly recalls. Previous research in anomaly detection (AD) has relied on prior knowledge for designing algorithms, which often requires extensive manual annotations, significant computing power, and large amounts of data for training. Autoregressive, multimodal Vision Language Models (AVLMs) offer a promising alternative due to their exceptional performance in visual reasoning across various domains. Despite this, their application to logical AD remains unexplored. In this work, we investigate using AVLMs for logical AD and demonstrate that they are well-suited to the task. Combining AVLMs with format embedding and a logic reasoner, we achieve SOTA performance on public benchmarks, MVTec LOCO AD, with an AUROC of 86.0% and F1-max of 83.7%, along with explanations of anomalies. This significantly outperforms the existing SOTA method by a large margin.
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2025 1verdicts
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LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection
A 3B multimodal model fine-tuned with supervised learning and GRPO achieves accuracy and F1 comparable to a 72B model on logical anomaly detection while generating structured textual explanations.