PaperCVPR 2022 · 2022

Towards Total Recall in Industrial Anomaly Detection

Karsten Roth Latha Pemula Joaquin Zepeda Bernhard Schölkopf Thomas Brox Peter Gehler

arXiv 2106.08265 DOI View in graph

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PatchCore

Training-free industrial anomaly detection: a single forward pass over defect-free images builds a coreset-subsampled memory bank of locally aware mid-level CNN patch features, and test images are scored by reweighted nearest-neighbour distance in that feature space.

Cited as a reference by 2 pages, grouped by domain

Anomaly detection 2

EfficientAD Visual Anomaly Detection

Appears in 2 narratives

NarrativeFoundation Models for Vision NarrativeVision Under a Latency Budget

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2019MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly DetectionBergmann, Fauser et al.2020Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent EmbeddingsBergmann, Fauser et al.

Built upon by · 4

2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesBatzner, Heckler, König 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and SegmentationJeong, Zou et al.2023SimpleNet: A Simple Network for Image Anomaly Detection and LocalizationLiu, Zhou et al.2023Asymmetric Student-Teacher Networks for Industrial Anomaly DetectionRudolph, Wehrbein et al.