IEEE

ViGLAD: Vision Graph Neural Networks for Logical Anomaly Detection

Abstract: Quality inspection is an industrial field with a growing interest in anomaly detection research. An anomaly in an image can either be structural or logical. While structural anomalies lie on ...
GitHub

MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning (ICCV2025)

This software is a research prototype, solely developed for and published as part of the publication MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot ...
GitHub

[WACV2025] AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2

This is the official code to reproduce the experiments in the paper AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2, accepted at IEEE/CVF Winter Conference on Applications of ...
IEEE

Anomaly Detection in Hyperspectral Images Using Adaptive Graph Frequency Location

Abstract: Graph theory-based techniques have recently been adopted for anomaly detection in hyperspectral images (HSIs). However, these methods rely excessively on the relational structure within the ...