Cover Image

The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Heckler-Kram, Lars, Neudeck, Jan-Hendrik, Scheler, Ulla, König, Rebecca, Steger, Carsten
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
by: Heckler-Kram, Lars, et al.
Published: (2025)

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
by: Batzner, Kilian, et al.
Published: (2023)

PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation
by: Zhang, Jian, et al.
Published: (2023)

IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection
by: Guo, Bingyang, et al.
Published: (2025)

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
by: He, Haoyang, et al.
Published: (2024)

MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection
by: Beizaee, Farzad, et al.
Published: (2025)

ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects
by: Krassnig, Paul J., et al.
Published: (2025)

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection
by: Bai, Haoyu, et al.
Published: (2025)

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection
by: Zhou, Ziqing, et al.
Published: (2025)

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data
by: Im, Jiin, et al.
Published: (2024)

CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset
by: Arodi, Akshatha, et al.
Published: (2024)

Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development
by: Lei, Tianwu, et al.
Published: (2024)

DINO-AD: Unsupervised Anomaly Detection with Frozen DINO-V3 Features
by: Huo, Jiayu, et al.
Published: (2026)

VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction
by: Nakata, Hiroto, et al.
Published: (2026)

Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors
by: Zhai, Guangyao, et al.
Published: (2025)

U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord
by: Zhang, Qi, et al.
Published: (2025)

MVTOP: Multi-View Transformer-based Object Pose-Estimation
by: Ranftl, Lukas, et al.
Published: (2025)

DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios
by: Kollias, Dimitrios, et al.
Published: (2025)

Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving
by: Bogdoll, Daniel, et al.
Published: (2024)

Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)

Advancing Video Anomaly Detection: A Concise Review and a New Dataset
by: Zhu, Liyun, et al.
Published: (2024)

Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection
by: Sun, Yihan, et al.
Published: (2026)

MoViAD: A Modular Library for Visual Anomaly Detection
by: Barusco, Manuel, et al.
Published: (2025)

GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
by: Sträter, Luc P. J., et al.
Published: (2024)

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
by: Zhao, Shifang, et al.
Published: (2025)

HomographyAD: Deep Anomaly Detection Using Self Homography Learning
by: Seok, Jongyub, et al.
Published: (2025)

COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection
by: Liao, Jingyi, et al.
Published: (2024)

CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection
by: Hsieh, Yu-Hsuan, et al.
Published: (2024)

Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
by: Zhou, Mingle, et al.
Published: (2026)

Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2024)

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
by: Yuan, Jingyi, et al.
Published: (2025)

Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2025)

DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
by: Zhang, Hui, et al.
Published: (2023)

ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection
by: Liang, Yun, et al.
Published: (2024)

InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models
by: Sakai, Shunsuke, et al.
Published: (2025)

Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling
by: Wu, Di, et al.
Published: (2024)

Vision-Language Models Assisted Unsupervised Video Anomaly Detection
by: Jiang, Yalong, et al.
Published: (2024)

Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection
by: Iqbal, Nasar, et al.
Published: (2025)

Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
by: Yang, Hui-Yue, et al.
Published: (2024)

AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving
by: Genilotti, Fabrizio, et al.
Published: (2026)