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Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspection, and medical lesion detection.
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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Unsupervised anomaly segmentation via deep feature reconstruction
Yong Shi, Jie Yang, and Zhiquan Qi · 2020
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Safe robot navigation via multi-modal anomaly detection
Lorenz Wellhausen, René Ranftl, and Marco Hutter · 2020
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Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows
Denis A. Gudovskiy, Shun Ishizaka, and Kazuki Kozuka · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, et al · 2021
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A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, et al · 2021
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DRAEM – A discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2021
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The medical segmentation decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M Summers, et al · 2022
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Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger · 2022
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The MVTec 3d-AD dataset for unsupervised 3d anomaly detection and localization
Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger · 2022
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Anomaly detection in autonomous driving: A survey
Daniel Bogdoll, Maximilian Nitsche, and J Marius Zöllner · 2022
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The eyecandies dataset for unsupervised multimodal anomaly detection and localization
Luca Bonfiglioli, Marco Toschi, Davide Silvestri, Nicola Fioraio, and Daniele De Gregorio · 2022
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Deep one-class classification via interpolated gaussian descriptor
Yuanhong Chen, Yu Tian, Guansong Pang, and Gustavo Carneiro · 2022
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Anomaly detection via reverse distillation from one-class embedding
Hanqiu Deng and Xingyu Li · 2022
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Catching both gray and black swans: Open-set supervised anomaly detection
Choubo Ding, Guansong Pang, and Chunhua Shen · 2022
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Registration based few-shot anomaly detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratlin, and Yanfeng Wang · 2022
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Softpatch: Unsupervised anomaly detection with noisy data
Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, WU Kai, Y. Liu, Chengjie Wang, and Feng Zheng · 2022
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Deep learning for anomaly detection: A review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel · 2022
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Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2022
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Asymmetric student-teacher networks for industrial anomaly detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2022
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Deep learning for unsupervised anomaly localization in industrial images: A survey
Xian Tao, Xinyi Gong, Xin Zhang, Shaohua Yan, and Chandranath Adak · 2022
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Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer · 2022
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Sequential modeling enables scalable learning for large vision models
Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, and Alexei A Efros · 2023
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Anomaly detection in 3d point clouds using deep geometric descriptors
Paul Bergmann and David Sattlegger · 2023
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A discrepancy aware framework for robust anomaly detection
Yuxuan Cai, Dingkang Liang, Dongliang Luo, Xinwei He, Xin Yang, and Xiang Bai · 2023
Cited alongside, same era.
Collaborative discrepancy optimization for reliable image anomaly localization
Yunkang Cao, Xiaohao Xu, Zhaoge Liu, and Weiming Shen · 2023
Cited alongside, same era.
Complementary pseudo multimodal feature for point cloud anomaly detection
Yunkang Cao, Xiaohao Xu, and Weiming Shen · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Bias: Incorporating biased knowledge to boost unsupervised image anomaly localization
Yunkang Cao, Xiaohao Xu, Chen Sun, Liang Gao, and Weiming Shen · 2023
Cited alongside, same era.
Gpt-4v(ision) system card
OpenAI · 2023
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Toward closed-loop additive manufacturing: Paradigm shift in fabrication, inspection, and repair
Manpreet Singh, Fujun Ruan, Albert Xu, Yuchen Wu, Archit Rungta, Luyuan Wang, Kevin Song, Howie Choset, and Lu Li · 2023
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Vipergpt: Visual inference via python execution for reasoning
Dídac Surís, Sachit Menon, and Carl Vondrick · 2023
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Multimodal industrial anomaly detection via hybrid fusion
Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, and Chengjie Wang · 2023
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Pushing the limits of fewshot anomaly detection in industry vision: Graphcore
Guoyang Xie, Jingbao Wang, Jiaqi Liu, Feng Zheng, and Yaochu Jin · 2023
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Multimodal learning with transformers: A survey
Peng Xu, Xiatian Zhu, and David A. Clifton · 2023
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Yunkang Cao, Xiaohao Xu, Chen Sun, Xiaonan Huang, and Weiming Shen · 2023
Cited alongside, same era.
Easynet: An easy network for 3d industrial anomaly detection
Rui Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang, Ziqi Luo, Jinfan Wang, and Feng Zheng · 2023
Cited alongside, same era.
Xuhai Chen, Yue Han, and Jiangning Zhang · 2023
Cited alongside, same era.
Shape-guided dual-memory learning for 3d anomaly detection
Yu-Min Chu, Chieh Liu, Ting-I Hsieh, Hwann-Tzong Chen, and Tyng-Luh Liu · 2023
Cited alongside, same era.
A survey of methods for automated quality control based on images
Jan Diers and Christian Pigorsch · 2023
Cited alongside, same era.
Few-shot defect image generation via defect-aware feature manipulation
Yuxuan Duan, Yan Hong, Li Niu, and Liqing Zhang · 2023
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
Cited alongside, same era.
Later among the works it cites.
The dawn of lmms: Preliminary explorations with gpt-4v (ision)
Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang · 2023
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Learning global-local correspondence with semantic bottleneck for logical anomaly detection
Haiming Yao, Wenyong Yu, Wei Luo, Zhenfeng Qiang, Donghao Luo, and Xiaotian Zhang · 2023
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Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection
Xincheng Yao, Ruoqi Li, Jing Zhang, Jun Sun, and Chongyang Zhang · 2023
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Prototypical residual networks for anomaly detection and localization
Hui Zhang, Zuxuan Wu, Zheng Wang, Zhineng Chen, and Yu-Gang Jiang · 2023
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Exploring plain vit reconstruction for multi-class unsupervised anomaly detection
Jiangning Zhang, Xuhai Chen, Yabiao Wang, Chengjie Wang, Yong Liu, Xiangtai Li, Ming-Hsuan Yang, and Dacheng Tao · 2023
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Exploring grounding potential of vqa-oriented gpt-4v for zero-shot anomaly detection
Jiangning Zhang, Xuhai Chen, Zhucun Xue, Yabiao Wang, Chengjie Wang, and Yong Liu · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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Pad: A dataset and benchmark for pose-agnostic anomaly detection
Qiang Zhou, Weize Li, Lihan Jiang, Guoliang Wang, Guyue Zhou, Shanghang Zhang, and Hao Zhao · 2023
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Efficientad: Accurate visual anomaly detection at millisecond-level latencies
Kilian Batzner, Lars Heckler, and Rebecca König · 2024
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Generating and reweighting dense contrastive patterns for unsupervised anomaly detection
Songmin Dai, Yifan Wu, Xiaoqiang Li, and Xiangyang Xue · 2024
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Diad: A diffusion-based framework for multi-class anomaly detection
Haoyang He, Jiangning Zhang, Hongxu Chen, Xuhai Chen, Zhishan Li, Xu Chen, Yabiao Wang, Chengjie Wang, and Lei Xie · 2024
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Few shot part segmentation reveals compositional logic for industrial anomaly detection
Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang, Ehsan Adeli, Kilian M. Pohl, and Sanghyun Park · 2024
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Unsupervised continual anomaly detection with contrastively-learned prompt
Jiaqi Liu, Kai Wu, Qiang Nie, Ying Chen, Bin-Bin Gao, Yong Liu, Jinbao Wang, Chengjie Wang, and Feng Zheng · 2024
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Template-based feature aggregation network for industrial anomaly detection
Wei Luo, Haiming Yao, and Wenyong Yu · 2024
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Cheating depth: Enhancing 3d surface anomaly detection via depth simulation
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2024
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Contextual affinity distillation for image anomaly detection
Jie Zhang, Masanori Suganuma, and Takayuki Okatani · 2024
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Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection
Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jiming Chen · 2024
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