Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts
This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …
single detection model that can generalize to detect anomalies in diverse datasets from …
Real3d-ad: A dataset of point cloud anomaly detection
High-precision point cloud anomaly detection is the gold standard for identifying the defects
of advancing machining and precision manufacturing. Despite some methodological …
of advancing machining and precision manufacturing. Despite some methodological …
ReConPatch: Contrastive patch representation learning for industrial anomaly detection
Anomaly detection is crucial to the advanced identification of product defects such as
incorrect parts, misaligned components, and damages in industrial manufacturing. Due to …
incorrect parts, misaligned components, and damages in industrial manufacturing. Due to …
RealNet: A feature selection network with realistic synthetic anomaly for anomaly detection
Self-supervised feature reconstruction methods have shown promising advances in
industrial image anomaly detection and localization. Despite this progress these methods …
industrial image anomaly detection and localization. Despite this progress these methods …
Anomaly heterogeneity learning for open-set supervised anomaly detection
Open-set supervised anomaly detection (OSAD)-a recently emerging anomaly detection
area-aims at utilizing a few samples of anomaly classes seen during training to detect …
area-aims at utilizing a few samples of anomaly classes seen during training to detect …
Bmad: Benchmarks for medical anomaly detection
Anomaly detection (AD) is a fundamental research problem in machine learning and
computer vision with practical applications in industrial inspection video surveillance and …
computer vision with practical applications in industrial inspection video surveillance and …
Exploring plain vit reconstruction for multi-class unsupervised anomaly detection
This work studies the recently proposed challenging and practical Multi-class Unsupervised
Anomaly Detection (MUAD) task, which only requires normal images for training while …
Anomaly Detection (MUAD) task, which only requires normal images for training while …
Bias: Incorporating biased knowledge to boost unsupervised image anomaly localization
Image anomaly localization is a pivotal technique in industrial inspection, often manifesting
as a supervised task where abundant normal samples coexist with rare abnormal samples …
as a supervised task where abundant normal samples coexist with rare abnormal samples …
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Logical anomalies (LA) refer to data violating underlying logical constraints eg, the quantity,
arrangement, or composition of components within an image. Detecting accurately such …
arrangement, or composition of components within an image. Detecting accurately such …
Long-Tailed Anomaly Detection with Learnable Class Names
Anomaly detection (AD) aims to identify defective images and localize their defects (if any).
Ideally AD models should be able to detect defects over many image classes; without relying …
Ideally AD models should be able to detect defects over many image classes; without relying …