SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation

• Published in: Computer Vision - ECCV 2022 Vol. 13690; pp. 392 - 408
• Main Authors: Zou, Yang, Jeong, Jongheon, Pemula, Latha, Zhang, Dongqing, Dabeer, Onkar
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 01.01.2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: Anomaly detection; Anomaly segmentation; Industrial anomaly dataset; Pre-training; Representation learning
• ISBN: 9783031200557; 3031200551
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-20056-4_23

Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we present a new dataset as well as a new self-supervised learning method for ImageNet pre-training to improve anomaly detection and segmentation in 1-class and 2-class 5/10/high-shot training setups. We release the Visual Anomaly (VisA) Dataset consisting of 10,821 high-resolution color images (9,621 normal and 1,200 anomalous samples) covering 12 objects in 3 domains, making it the largest industrial anomaly detection dataset to date. Both image and pixel-level labels are provided. We also propose a new self-supervised framework - SPot-the-difference (SPD) - which can regularize contrastive self-supervised pre-training, such as SimSiam, MoCo and SimCLR, to be more suitable for anomaly detection tasks. Our experiments on VisA and MVTec-AD dataset show that SPD consistently improves these contrastive pre-training baselines and even the supervised pre-training. For example, SPD improves Area Under the Precision-Recall curve (AU-PR) for anomaly segmentation by 5.9% and 6.8% over SimSiam and supervised pre-training respectively in the 2-class high-shot regime. We open-source the project at http://github.com/amazon-research/spot-diff.