What makes a good data augmentation for few-shot unsupervised image anomaly detection?

6 April 2023

Papers citing "What makes a good data augmentation for few-shot unsupervised image anomaly detection?"

8 / 8 papers shown

Title
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Wenbing Zhu Lidong Wang Ziqing Zhou Chengjie Wang Yurui Pan ... Yulong Chen Shuguang Qian M. Chi Bo Peng Lizhuang Ma 26 1 0 19 Apr 2025
Real3D-AD: A Dataset of Point Cloud Anomaly Detection Jiaqi Liu Guoyang Xie Rui Chen Xinpeng Li Jinbao Wang Yong-Jin Liu Chengjie Wang Feng Zheng 32 41 0 23 Sep 2023
Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond Chen Shuai Meng Fanman Runtong Zhang Heqian Qiu Hongliang Li Wu Qingbo Xu Linfeng VLM 30 12 0 15 Aug 2023
Optimizing PatchCore for Few/many-shot Anomaly Detection Joao Santos Triet Tran Oliver Rippel 26 10 0 20 Jul 2023
Anomaly Detection via Reverse Distillation from One-Class Embedding Hanqiu Deng Xingyu Li UQCV 117 448 0 26 Jan 2022
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection Marco Rudolph Tom Wehrbein Bodo Rosenhahn Bastian Wandt UQCV 78 208 0 06 Oct 2021
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization P. Mishra Riccardo Verk Daniele Fornasier C. Piciarelli G. Foresti ViT 80 287 0 20 Apr 2021
DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation Jie Yang Yong Shi Zhiquan Qi UQCV 109 115 0 13 Dec 2020