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

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arxiv 2207.14315 v1 pith:DHMOXA56 submitted 2022-07-28 cs.CV

Yang Zou , Jongheon Jeong , Latha Pemula , Dongqing Zhang , Onkar Dabeer This is my paper
classification cs.CV
keywords anomalypre-trainingdetectiondatasetself-supervisedclasssegmentationcontrastive
verification ladder T0 review T1 audit T2 compute T3 formal
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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 .

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark

    cs.CV 2025-06 conditional novelty 7.0 of 10

    SiM3D provides a multiview, multimodal 3D anomaly detection benchmark with single-instance training and synthetic-to-real evaluation, showing adapted 2D methods often beat multimodal 3D methods on the new voxel-based task.

  2. Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection

    cs.CV 2025-08 conditional novelty 6.0 of 10

    SNARM combines memory-bank residuals, self-referential in-image residuals, and residual-guided Mamba scanning to report state-of-the-art anomaly detection scores on three benchmarks.

  3. MoViAD: A Modular Library for Visual Anomaly Detection

    cs.CV 2025-07 reject novelty 3.0 of 10

    A modular visual anomaly detection library is described, but without code, benchmarks, or experimental validation of its capabilities.

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