LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 4129–4137

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 an F1-max of 83.7% along with explanations of the anomalies. This significantly outperforms the existing SOTA method by 18.1% in AUROC and 4.6% in F1-max score.

Authors 5

1. Yongli Mou Aachen Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

   Department of Computer Science, RWTH Aachen University, Aachen, Germany

2. Gerhard Lakemeyer Aachen Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

   Department of Computer Science, RWTH Aachen University, Aachen, Germany

3. Stefan Decker Aachen Department of Computer Science

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Department of Computer Science, RWTH Aachen University, Aachen, Germany Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

4. Oliver Simons
   Affiliation as printed

   Independent Researcher

5. Johannes Stegmaier Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany