Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Yu, Wenyong"'
Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multi-class anomaly detection, w
Externí odkaz:
http://arxiv.org/abs/2406.11507
Texture surface anomaly detection finds widespread applications in industrial settings. However, existing methods often necessitate gathering numerous samples for model training. Moreover, they predominantly operate within a close-set detection frame
Externí odkaz:
http://arxiv.org/abs/2406.07333
The unsupervised visual inspection of defects in industrial products poses a significant challenge due to substantial variations in product surfaces. Current unsupervised models struggle to strike a balance between detecting texture and object defect
Externí odkaz:
http://arxiv.org/abs/2311.06504
In this paper, we introduce the novel state-of-the-art Dual-attention Transformer and Discriminative Flow (DADF) framework for visual anomaly detection. Based on only normal knowledge, visual anomaly detection has wide applications in industrial scen
Externí odkaz:
http://arxiv.org/abs/2303.17882
This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has become an active research area in various real-world applications, such as
Externí odkaz:
http://arxiv.org/abs/2303.05768
Autor:
Yao, Haiming, Yu, Wenyong
Industrial vision anomaly detection plays a critical role in the advanced intelligent manufacturing process, while some limitations still need to be addressed under such a context. First, existing reconstruction-based methods struggle with the identi
Externí odkaz:
http://arxiv.org/abs/2211.12311
Visual anomaly detection plays a significant role in the development of industrial automatic product quality inspection. As a result of the utmost imbalance in the amount of normal and abnormal data, growing attention has been given to unsupervised m
Externí odkaz:
http://arxiv.org/abs/2211.10060
Unsupervised visual anomaly detection conveys practical significance in many scenarios and is a challenging task due to the unbounded definition of anomalies. Moreover, most previous methods are application-specific, and establishing a unified model
Externí odkaz:
http://arxiv.org/abs/2211.00349
In surface defect detection, due to the extreme imbalance in the number of positive and negative samples, positive-samples-based anomaly detection methods have received more and more attention. Specifically, reconstruction-based methods are the most
Externí odkaz:
http://arxiv.org/abs/2208.03879
Recent advances in the industrial inspection of textured surfaces-in the form of visual inspection-have made such inspections possible for efficient, flexible manufacturing systems. We propose an unsupervised feature memory rearrangement network (FMR
Externí odkaz:
http://arxiv.org/abs/2206.10830