Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection
Fuente:
arXiv
Saved in:
| Main Authors: | Baitieva, Aimira, Bouaouni, Yacine, Briot, Alexandre, Ameln, Dick, Khalfaoui, Souhaiel, Akcay, Samet |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble
by: Rolih, Blaž, et al.
Published: (2024)
by: Rolih, Blaž, et al.
Published: (2024)
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance
by: Bertoldo, Joao P. C., et al.
Published: (2024)
by: Bertoldo, Joao P. C., et al.
Published: (2024)
Supervised Anomaly Detection for Complex Industrial Images
by: Baitieva, Aimira, et al.
Published: (2024)
by: Baitieva, Aimira, et al.
Published: (2024)
From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
by: Heckler-Kram, Lars, et al.
Published: (2025)
by: Heckler-Kram, Lars, et al.
Published: (2025)
MIRAGE: Model-agnostic Industrial Realistic Anomaly Generation and Evaluation for Visual Anomaly Detection
by: Hu, Jinwei, et al.
Published: (2026)
by: Hu, Jinwei, et al.
Published: (2026)
Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion
by: Hicsonmez, Samet, et al.
Published: (2026)
by: Hicsonmez, Samet, et al.
Published: (2026)
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection
by: Liu, Tongkun, et al.
Published: (2023)
by: Liu, Tongkun, et al.
Published: (2023)
FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection
by: Isaac-Medina, Brian K. S., et al.
Published: (2024)
by: Isaac-Medina, Brian K. S., et al.
Published: (2024)
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
by: Xie, Guoyang, et al.
Published: (2023)
by: Xie, Guoyang, et al.
Published: (2023)
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark
by: Wu, Gaochang, et al.
Published: (2024)
by: Wu, Gaochang, et al.
Published: (2024)
Hierarchy-Aware Fine-Tuning of Vision-Language Models
by: Li, Jiayu, et al.
Published: (2025)
by: Li, Jiayu, et al.
Published: (2025)
VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion
by: Hicsonmez, Samet, et al.
Published: (2025)
by: Hicsonmez, Samet, et al.
Published: (2025)
PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detection
by: Huang, Po-Han, et al.
Published: (2025)
by: Huang, Po-Han, et al.
Published: (2025)
Dilated Convolution with Learnable Spacings
by: Khalfaoui-Hassani, Ismail
Published: (2024)
by: Khalfaoui-Hassani, Ismail
Published: (2024)
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection
by: Zhang, Jiangning, et al.
Published: (2024)
by: Zhang, Jiangning, et al.
Published: (2024)
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
by: Weatherly, Chad, et al.
Published: (2026)
by: Weatherly, Chad, et al.
Published: (2026)
Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation
by: Barfoot, Theodore, et al.
Published: (2025)
by: Barfoot, Theodore, et al.
Published: (2025)
Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices
by: Lin, Junyan, et al.
Published: (2025)
by: Lin, Junyan, et al.
Published: (2025)
IPAD: Industrial Process Anomaly Detection Dataset
by: Liu, Jinfan, et al.
Published: (2024)
by: Liu, Jinfan, et al.
Published: (2024)
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)
by: Tong, Xuan, et al.
Published: (2025)
ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining
by: Yao, Xincheng, et al.
Published: (2025)
by: Yao, Xincheng, et al.
Published: (2025)
IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection
by: Li, Zewen, et al.
Published: (2025)
by: Li, Zewen, et al.
Published: (2025)
GATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly Detection
by: Psiris, Aggelos, et al.
Published: (2026)
by: Psiris, Aggelos, et al.
Published: (2026)
SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection
by: Fu, Chenhao, et al.
Published: (2026)
by: Fu, Chenhao, et al.
Published: (2026)
On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection
by: Le-Gia, Tai, et al.
Published: (2025)
by: Le-Gia, Tai, et al.
Published: (2025)
Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions
by: Barusco, Manuel, et al.
Published: (2026)
by: Barusco, Manuel, et al.
Published: (2026)
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
by: Wang, Chengjie, et al.
Published: (2024)
by: Wang, Chengjie, et al.
Published: (2024)
MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
by: Jiang, Xi, et al.
Published: (2024)
by: Jiang, Xi, et al.
Published: (2024)
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
by: Chen, Qiyu, et al.
Published: (2025)
by: Chen, Qiyu, et al.
Published: (2025)
ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection
by: Zhang, Qunyi, et al.
Published: (2025)
by: Zhang, Qunyi, et al.
Published: (2025)
Efficient Visual Anomaly Detection at the Edge: Enabling Real-Time Industrial Inspection on Resource-Constrained Devices
by: Stropeni, Arianna, et al.
Published: (2026)
by: Stropeni, Arianna, et al.
Published: (2026)
ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
by: Hermary, Romain, et al.
Published: (2026)
by: Hermary, Romain, et al.
Published: (2026)
Text-Guided Multimodal Unified Industrial Anomaly Detection
by: Li, Zewen, et al.
Published: (2026)
by: Li, Zewen, et al.
Published: (2026)
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
by: Costanzino, Alex, et al.
Published: (2023)
by: Costanzino, Alex, et al.
Published: (2023)
Towards High-Resolution Industrial Image Anomaly Detection
by: Zhang, Ximiao, et al.
Published: (2025)
by: Zhang, Ximiao, et al.
Published: (2025)
Multimodal Industrial Anomaly Detection via Geometric Prior
by: Li, Min, et al.
Published: (2026)
by: Li, Min, et al.
Published: (2026)
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection
by: Chen, Xin, et al.
Published: (2024)
by: Chen, Xin, et al.
Published: (2024)
Deep Industrial Image Anomaly Detection: A Survey
by: Liu, Jiaqi, et al.
Published: (2023)
by: Liu, Jiaqi, et al.
Published: (2023)
Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
by: Chen, Qiyu, et al.
Published: (2024)
by: Chen, Qiyu, et al.
Published: (2024)
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
by: Chen, Qiyu, et al.
Published: (2024)
by: Chen, Qiyu, et al.
Published: (2024)
Similar Items
-
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble
by: Rolih, Blaž, et al.
Published: (2024) -
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance
by: Bertoldo, Joao P. C., et al.
Published: (2024) -
Supervised Anomaly Detection for Complex Industrial Images
by: Baitieva, Aimira, et al.
Published: (2024) -
From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
by: Heckler-Kram, Lars, et al.
Published: (2025) -
MIRAGE: Model-agnostic Industrial Realistic Anomaly Generation and Evaluation for Visual Anomaly Detection
by: Hu, Jinwei, et al.
Published: (2026)