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 …
Simplenet: A simple network for image anomaly detection and localization
Z Liu, Y Zhou, Y Xu, Z Wang - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
We propose a simple and application-friendly network (called SimpleNet) for detecting and
localizing anomalies. SimpleNet consists of four components:(1) a pre-trained Feature …
localizing anomalies. SimpleNet consists of four components:(1) a pre-trained Feature …
Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we
present a new dataset as well as a new self-supervised learning method for ImageNet pre …
present a new dataset as well as a new self-supervised learning method for ImageNet pre …
Winclip: Zero-/few-shot anomaly classification and segmentation
Visual anomaly classification and segmentation are vital for automating industrial quality
inspection. The focus of prior research in the field has been on training custom models for …
inspection. The focus of prior research in the field has been on training custom models for …
Error detection in egocentric procedural task videos
We present a new egocentric procedural error dataset containing videos with various types
of errors as well as normal videos and propose a new framework for procedural error …
of errors as well as normal videos and propose a new framework for procedural error …
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 …
Anomaly detection using edge computing in video surveillance system
The current concept of smart cities influences urban planners and researchers to provide
modern, secured and sustainable infrastructure and gives a decent quality of life to its …
modern, secured and sustainable infrastructure and gives a decent quality of life to its …
SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was
recently introduced in literature. Due to its highly accurate results, the method attracted the …
recently introduced in literature. Due to its highly accurate results, the method attracted the …
Softpatch: Unsupervised anomaly detection with noisy data
Although mainstream unsupervised anomaly detection (AD) algorithms perform well in
academic datasets, their performance is limited in practical application due to the ideal …
academic datasets, their performance is limited in practical application due to the ideal …