Fetching the paper…

Reading the bibliography…

2023

Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection

Huang, Chaoqin, Jiang, Aofan, Zhang, Ya et al.

Original
PDF
Post on X

Understand

Anomaly detection has gained considerable attention due to its broad range of applications, particularly in industrial defect detection.

To address the challenges of data collection, researchers have introduced zero-/few-shot anomaly detection techniques that require minimal normal images for each category.
However, complex industrial scenarios often involve multiple objects, presenting a significant challenge.
In light of this, we propose a straightforward yet powerful multi-scale memory comparison framework for zero-/few-shot anomaly detection.

Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection · Around

Built on

Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Earlier work this paper cites.
Same same but differnet: Semi-supervised defect detection with normalizing flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn · 2021
Earlier work this paper cites.
A hierarchical transformation-discriminating generative model for few shot anomaly detection
Shelly Sheynin, Sagie Benaim, and Lior Wolf · 2021
Earlier work this paper cites.

Similar

Registration based few-shot anomaly detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, and Yan-Feng Wang · 2022
Cited alongside, same era.
Self-supervised masking for unsupervised anomaly detection and localization
Chaoqin Huang, Qinwei Xu, Yanfeng Wang, Yu Wang, and Ya Zhang · 2022
Cited alongside, same era.
Attribute restoration framework for anomaly detection
Fei Ye, Chaoqin Huang, Jinkun Cao, Maosen Li, Ya Zhang, and Cewu Lu · 2022
Cited alongside, same era.

Then

Winclip: Zero-/few-shot anomaly classification and segmentation
Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer · 2023
Closest in time.
Segment anything
Original
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…