FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection | AD Research Hub

AD Research Hub

Dashboard People Papers Workshops Datasets Research Map

⌘K

AD Research Hub — Anomaly Detection in Computer Vision

← Back to Papers

FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection

Authors

Zhonghang Liu, Kun Zhou, Changshuo Wang, Wen-Yan Lin, Jiangbo Lu

CVPR-2025direct anomaly

Score

13

Tags

outlier detection

Links

Cite

Related Papers

ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches

Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif +2

ICCV-2025direct anomaly13

outlier detection

Track Any Anomalous Object:A Granular Video Anomaly Detection Pipeline

Yuzhi Huang, Chenxin Li, Haitao Zhang +9

CVPR-2025direct anomaly34

anomaly detectionanomalousvideo anomaly

Anomize: Better Open Vocabulary Video Anomaly Detection

Fei Li, Wenxuan Liu, Jingjing Chen +4

CVPR-2025direct anomaly24

anomaly detectionvideo anomaly

Just Dance with pi! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection

Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva +5

CVPR-2025direct anomaly24

anomaly detectionvideo anomaly

Noise-Resistant Video Anomaly Detection via RGB Error-Guided Multiscale Predictive Coding and Dynamic Memory

Han Hu, Wenli Du, Peng Liao +2

CVPR-2025direct anomaly24

anomaly detectionvideo anomaly

PDF

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection

Wenbing Zhu, Lidong Wang, Ziqing Zhou +14

CVPR-2025direct anomaly24

anomaly detectionindustrial anomaly

Real-IAD

PDF

Sign in to access this content