Sub-Image Anomaly Detection with Deep Pyramid Correspondences

• URL: http://arxiv.org/abs/2005.02357v3
• Date: Wed, 3 Feb 2021 16:28:51 GMT
• Title: Sub-Image Anomaly Detection with Deep Pyramid Correspondences
• Authors: Niv Cohen and Yedid Hoshen
• Abstract summary: Nearest neighbor (kNN) methods exhibit very strong anomaly detection performance when applied to entire images. We present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid.
• Score: 39.59606869996232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image. In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid. SPADE is shown to achieve state-of-the-art performance on unsupervised anomaly detection and localization while requiring virtually no training time.

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