CUT: A Controllable, Universal, and Training-Free Visual Anomaly
Generation Framework

CUT: A Controllable, Universal, and Training-Free Visual Anomaly Generation Framework

3 June 2024

Olga Fink

Papers citing "CUT: A Controllable, Universal, and Training-Free Visual Anomaly Generation Framework"

7 / 7 papers shown

Title
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection Xiaofan Li Zhizhong Zhang Xin Tan Chengwei Chen Yanyun Qu Yuan Xie Lizhuang Ma VLM 32 5 0 08 Apr 2024
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect Yunkang Cao Xiaohao Xu Jiangning Zhang Yuqi Cheng Xiaonan Huang Guansong Pang Weiming Shen 60 19 0 29 Jan 2024
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model Teng Hu Jiangning Zhang Ran Yi Yuzhen Du Xu Chen Liang Liu Yabiao Wang Chengjie Wang 57 21 0 10 Dec 2023
SimpleNet: A Simple Network for Image Anomaly Detection and Localization Zhikang Liu Yiming Zhou Yuansheng Xu Zilei Wang 41 120 0 27 Mar 2023
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation Jongheon Jeong Yang Zou Taewan Kim Dongqing Zhang Avinash Ravichandran O. Dabeer VLM 47 92 0 26 Mar 2023
Anomaly Detection via Reverse Distillation from One-Class Embedding Hanqiu Deng Xingyu Li UQCV 75 299 0 26 Jan 2022
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization Hannah M. Schlüter Jeremy Tan Benjamin Hou Bernhard Kainz 84 82 0 30 Sep 2021