Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection
Toshimichi Aota, Lloyd Teh Tzer Tong, Takayuki Okatani
Abstract
Research on unsupervised anomaly detection (AD) has recently progressed, significantly increasing detection accuracy. This paper focuses on texture images and considers how few normal samples are needed for accurate AD. We first highlight the critical nature of the problem that previous studies have overlooked: accurate detection gets harder for anisotropic textures when image orientations are not aligned between inputs and normal samples. We then propose a zero-shot method, which detects anomalies without using a normal sample. The method is free from the issue of unaligned orientation between input and normal images. It assumes the input texture to be homogeneous, detecting image regions that break the homogeneity as anomalies. We present a quantitative criterion to judge whether this assumption holds for an input texture. Experimental results show the broad applicability of the proposed zero-shot method and its good performance comparable to or even higher than the state-of-the-art methods using hundreds of normal samples. The code and data are available from https://drive.google.com/drive/folders/10OyPzvI3H6llCZBxKxFlKWt1Pw1tkMK1.