A Discrepancy Aware Framework for Robust Anomaly Detection

A Discrepancy Aware Framework for Robust Anomaly Detection

11 October 2023

Dingkang Liang

Xin Yang

Papers citing "A Discrepancy Aware Framework for Robust Anomaly Detection"

9 / 9 papers shown

Title
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection Jiajie Quan Ao Tong Yuxuan Cai Xinwei He Y. Wang Yang Zhou 52 0 0 27 Mar 2025
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection Yunkang Cao Jiangning Zhang Luca Frittoli Yuqi Cheng Weiming Shen Giacomo Boracchi VLM 33 27 0 22 Jul 2024
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection Haiming Yao Yunkang Cao Wei Luo Weihang Zhang Wenyong Yu Weiming Shen 26 7 0 17 Jun 2024
PixOOD: Pixel-Level Out-of-Distribution Detection Tomávs Vojívr Jan Sochman Jivrí Matas OODD 22 9 0 30 May 2024
SoftPatch: Unsupervised Anomaly Detection with Noisy Data Xi Jiang Ying Chen Qiang Nie Y. Liu Jianlin Liu Jinbao Wang WU Kai Chengjie Wang Feng Zheng 124 34 0 21 Mar 2024
Segment Any Anomaly without Training via Hybrid Prompt Regularization Yunkang Cao Xiaohao Xu Chen Sun Y. Cheng Zongwei Du Liang Gao Weiming Shen VLM 24 69 0 18 May 2023
Anomaly Detection via Reverse Distillation from One-Class Embedding Hanqiu Deng Xingyu Li UQCV 101 299 0 26 Jan 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,337 0 11 Nov 2021
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization Hannah M. Schlüter Jeremy Tan Benjamin Hou Bernhard Kainz 118 82 0 30 Sep 2021