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The vision-language model has brought great improvement to few-shot industrial anomaly detection, which usually needs to design of hundreds of prompts through prompt engineering.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Sub-image anomaly detection with deep pyramid correspondences
Niv Cohen and Yedid Hoshen · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Patch svdd: Patch-level svdd for anomaly detection and segmentation
Jihun Yi and Sungroh Yoon · 2020
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Padim: a patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 2021
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Openclip, 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions
Stepan Jezek, Martin Jonak, Radim Burget, Pavel Dvorak, and Milos Skotak · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Deep dual consecutive network for human pose estimation
Zhenguang Liu, Haoming Chen, Runyang Feng, Shuang Wu, Shouling Ji, Bailin Yang, and Xun Wang · 2021
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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
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Same same but differnet: Semi-supervised defect detection with normalizing flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn · 2021
Cited alongside, same era.
Multiresolution knowledge distillation for anomaly detection
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H Rohban, and Hamid R Rabiee · 2021
Cited alongside, same era.
Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
Cited alongside, same era.
A hierarchical transformation-discriminating generative model for few shot anomaly detection
Shelly Sheynin, Sagie Benaim, and Lior Wolf · 2021
Cited alongside, same era.
Student-teacher feature pyramid matching for anomaly detection
Guodong Wang, Shumin Han, Errui Ding, and Di Huang · 2021
Cited alongside, same era.
Efficientad: Accurate visual anomaly detection at millisecond-level latencies
Kilian Batzner, Lars Heckler, and Rebecca König · 2023
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Segment any anomaly without training via hybrid prompt regularization
Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Zongwei Du, Liang Gao, and Weiming Shen · 2023
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Xuhai Chen, Yue Han, and Jiangning Zhang · 2023
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Anovl: Adapting vision-language models for unified zero-shot anomaly localization
Hanqiu Deng, Zhaoxiang Zhang, Jinan Bao, and Xingyu Li · 2023
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Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2021
Cited alongside, same era.
Vlmo: Unified vision-language pre-training with mixture-of-modality-experts
Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, Songhao Piao, and Furu Wei · 2022
Cited alongside, same era.
Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger · 2022
Cited alongside, same era.
Registration based few-shot anomaly detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael W. Spratling, and Yan-Feng Wang · 2022
Cited alongside, same era.
En-compactness: Self-distillation embedding & contrastive generation for generalized zero-shot learning
Xia Kong, Zuodong Gao, Xiaofan Li, Ming Hong, Jun Liu, Chengjie Wang, Yuan Xie, and Yanyun Qu · 2022
Cited alongside, same era.
Temporal feature alignment and mutual information maximization for video-based human pose estimation
Zhenguang Liu, Runyang Feng, Haoming Chen, Shuang Wu, Yixing Gao, Yunjun Gao, and Xiang Wang · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Fastrecon: Few-shot industrial anomaly detection via fast feature reconstruction
Zheng Fang, Xiaoyang Wang, Haocheng Li, Jiejie Liu, Qiugui Hu, and Jimin Xiao · 2023
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Clip-adapter: Better vision-language models with feature adapters
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao · 2023
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Remembering normality: Memory-guided knowledge distillation for unsupervised anomaly detection
Zhihao Gu, Liang Liu, Xu Chen, Ran Yi, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Annan Shu, Guannan Jiang, and Lizhuang Ma · 2023
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Template-guided hierarchical feature restoration for anomaly detection
Hewei Guo, Liping Ren, Jingjing Fu, Yuwang Wang, Zhizheng Zhang, Cuiling Lan, Haoqian Wang, and Xinwen Hou · 2023
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Winclip: Zero-/few-shot anomaly classification and segmentation
Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer · 2023
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Maple: Multi-modal prompt learning
Muhammad Uzair Khattak, Hanoona Abdul Rasheed, Muhammad Maaz, Salman H. Khan, and Fahad Shahbaz Khan · 2023
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Vs-boost: Boosting visual-semantic association for generalized zero-shot learning
Xiaofan Li, Yachao Zhang, Shiran Bian, Yanyun Qu, Yuan Xie, Zhongchao Shi, and Jianping Fan · 2023
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Simplenet: A simple network for image anomaly detection and localization
Zhikang Liu, Yiming Zhou, Yuansheng Xu, and Zilei Wang · 2023
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Inter-realization channels: Unsupervised anomaly detection beyond one-class classification
Declan McIntosh and Alexandra Branzan Albu · 2023
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Random word data augmentation with clip for zero-shot anomaly detection
Masato Tamura · 2023
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Few-shot learning with visual distribution calibration and cross-modal distribution alignment
Runqi Wang, Hao Zheng, Xiaoyue Duan, Jianzhuang Liu, Yuning Lu, Tian Wang, Songcen Xu, and Baochang Zhang · 2023
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Perturbed progressive learning for semisupervised defect segmentation
Yao Wu, Mingwei Xing, Yachao Zhang, Yuan Xie, Zongze Wu, and Yanyun Qu · 2023
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Zegclip: Towards adapting clip for zero-shot semantic segmentation
Ziqin Zhou, Yinjie Lei, Bowen Zhang, Lingqiao Liu, and Yifan Liu · 2023
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