异常检测
分割
背景(考古学)
弹丸
计算机科学
人工智能
一次性
异常(物理)
模式识别(心理学)
工程类
地质学
材料科学
物理
冶金
机械工程
古生物学
凝聚态物理
作者
Yuxin Jiang,Yunkang Cao,Weiming Shen
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-11
标识
DOI:10.1109/tnnls.2024.3463495
摘要
Few-shot anomaly detection (FSAD) denotes the identification of anomalies within a target category with a limited number of normal samples. Existing FSAD methods largely rely on pretrained feature representations to detect anomalies, but the inherent domain gap between pretrained representations and target FSAD scenarios is often overlooked. This study proposes a prototypical learning-guided context-aware segmentation network (PCSNet) to address the domain gap, thereby improving feature descriptiveness in target scenarios and enhancing FSAD performance. In particular, PCSNet comprises a prototypical feature adaption (PFA) subnetwork and a context-aware segmentation (CAS) subnetwork. PFA extracts prototypical features as guidance to ensure better feature compactness for normal data while distinct separation from anomalies. A pixel-level disparity classification (PDC) loss is also designed to make subtle anomalies more distinguishable. Then a CAS subnetwork is introduced for pixel-level anomaly localization, where pseudo anomalies are exploited to facilitate the training process. Experimental results on MVTec AD and metal part defect detection (MPDD) demonstrate the superior FSAD performance of PCSNet, with 94.9% and 80.2% image-level area under the receiver operating characteristics (AUROCs) in an eight-shot scenario, respectively. Real-world applications on automotive plastic part inspection further demonstrate that PCSNet can achieve promising results with limited training samples. The code is available at https://github.com/yuxin-jiang/PCSNet.
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