Deep learning for unsupervised anomaly localization in industrial images: A survey
X Tao, X Gong, X Zhang, S Yan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Currently, deep learning-based visual inspection has been highly successful with the help of
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …
Deep industrial image anomaly detection: A survey
The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
Prototypical residual networks for anomaly detection and localization
Anomaly detection and localization are widely used in industrial manufacturing for its
efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models …
efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models …
Anomalygpt: Detecting industrial anomalies using large vision-language models
Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have demonstrated
the capability of understanding images and achieved remarkable performance in various …
the capability of understanding images and achieved remarkable performance in various …
Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection
X Yao, R Li, J Zhang, J Sun… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Most anomaly detection (AD) models are learned using only normal samples in an
unsupervised way, which may result in ambiguous decision boundary and insufficient …
unsupervised way, which may result in ambiguous decision boundary and insufficient …
Self-supervised masked convolutional transformer block for anomaly detection
Anomaly detection has recently gained increasing attention in the field of computer vision,
likely due to its broad set of applications ranging from product fault detection on industrial …
likely due to its broad set of applications ranging from product fault detection on industrial …
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal
images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most …
images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most …
Industrial anomaly detection with domain shift: A real-world dataset and masked multi-scale reconstruction
Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The
diversity of the datasets is the foundation for developing comprehensive IAD algorithms …
diversity of the datasets is the foundation for developing comprehensive IAD algorithms …
Focus the discrepancy: Intra-and inter-correlation learning for image anomaly detection
Humans recognize anomalies through two aspects: larger patch-wise representation
discrepancies and weaker patch-to-normal-patch correlations. However, the previous AD …
discrepancies and weaker patch-to-normal-patch correlations. However, the previous AD …
Attention-conditioned augmentations for self-supervised anomaly detection and localization
B Bozorgtabar, D Mahapatra - Proceedings of the AAAI Conference on …, 2023 - ojs.aaai.org
Self-supervised anomaly detection and localization are critical to real-world scenarios in
which collecting anomalous samples and pixel-wise labeling is tedious or infeasible, even …
which collecting anomalous samples and pixel-wise labeling is tedious or infeasible, even …