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Object anomaly detection is an important problem in the field of machine vision and has seen remarkable progress recently.
Gdxray: The database of x-ray images for nondestructive testing
Domingo Mery, Vladimir Riffo, Uwe Zscherpel, German Mondragón, Iván Lillo, Irene Zuccar, Hans Lobel, and Miguel Carrasco · 2015
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Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
Hao Su, Charles R Qi, Yangyan Li, and Leonidas J Guibas · 2015
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Fully convolutional neural network for fast anomaly detection in crowded scenes
M Sabokrou, M Fayyaz, M Fathy, and R Klette · 2016
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
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Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes
Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox · 2017
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Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning
Max K Ferguson, AK Ronay, Yung-Tsun Tina Lee, and Kincho H Law · 2018
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Fine-tuning cnn image retrieval with no human annotation
Filip Radenović, Giorgos Tolias, and Ondřej Chum · 2018
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Deep autoencoding models for unsupervised anomaly segmentation in brain mr images
Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, and Nassir Navab · 2019
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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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Dream: deep recursive attentive model for anomaly detection in kernel events
Okwudili M Ezeme, Qusay H Mahmoud, and Akramul Azim · 2019
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Ocgan: One-class novelty detection using gans with constrained latent representations
Pramuditha Perera, Ramesh Nallapati, and Bing Xiang · 2019
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f-anogan: Fast unsupervised anomaly detection with generative adversarial networks
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Georg Langs, and Ursula Schmidt-Erfurth · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Deep image features for instance-level recognition and matching
André Araujo · 2020
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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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Unifying deep local and global features for image search
Bingyi Cao, Andre Araujo, and Jack Sim · 2020
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Sub-image anomaly detection with deep pyramid correspondences
Niv Cohen and Yedid Hoshen · 2020
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Anomaly localization by modeling perceptual features
David Dehaene and Pierre Eline · 2020
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Image anomaly detection using normal data only by latent space resampling
Lu Wang, Dongkai Zhang, Jiahao Guo, and Yuexing Han · 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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The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization
Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger · 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
Earlier work this paper cites.
Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister · 2021
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
Cited alongside, same era.
Vt-adl: A vision transformer network for image anomaly detection and localization
Pankaj Mishra, Riccardo Verk, Daniele Fornasier, Claudio Piciarelli, and Gian Luca Foresti · 2021
Cited alongside, same era.
Panda: Adapting pretrained features for anomaly detection and segmentation
Tal Reiss, Niv Cohen, Liron Bergman, and Yedid Hoshen · 2021
Cited alongside, same era.
Same same but differnet: Semi-supervised defect detection with normalizing flows
Marco Rudolph, Bastian Wandt, and Bodo Rosenhahn · 2021
Cited alongside, same era.
Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows
Denis Gudovskiy, Shun Ishizaka, and Kazuki Kozuka · 2022
Later among the works it cites.
Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization
Sungwook Lee, Seunghyun Lee, and Byung Cheol Song · 2022
Later among the works it cites.
Omni-frequency channel-selection representations for unsupervised anomaly detection
Yufei Liang, Jiangning Zhang, Shiwei Zhao, Runze Wu, Yong Liu, and Shuwen Pan · 2022
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Inpainting transformer for anomaly detection
Jonathan Pirnay and Keng Chai · 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
Later among the works it cites.
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Jouwon Song, Kyeongbo Kong, Ye-In Park, Seong-Gyun Kim, and Suk-Ju Kang · 2021
Cited alongside, same era.
imap: Implicit mapping and positioning in real-time
Edgar Sucar, Shikun Liu, Joseph Ortiz, and Andrew J Davison · 2021
Cited alongside, same era.
A multi-scale a contrario method for unsupervised image anomaly detection
Matias Tailanian, Pablo Musé, and Álvaro Pardo · 2021
Cited alongside, same era.
Student-teacher feature pyramid matching for unsupervised anomaly detection
Guodong Wang, Shumin Han, Errui Ding, and Di Huang · 2021
Cited alongside, same era.
Reconstruction student with attention for student-teacher pyramid matching
Shinji Yamada and Kazuhiro Hotta · 2021
Cited alongside, same era.
Learning semantic context from normal samples for unsupervised anomaly detection
Xudong Yan, Huaidong Zhang, Xuemiao Xu, Xiaowei Hu, and Pheng-Ann Heng · 2021
Cited alongside, same era.
Unsupervised anomaly segmentation via multilevel image reconstruction and adaptive attention-level transition
Yi Yan, Deming Wang, Guangliang Zhou, and Qijun Chen · 2021
Cited alongside, same era.
Fully convolutional cross-scale-flows for image-based defect detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2022
Later among the works it cites.
A unified model for multi-class anomaly detection
Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le · 2022
Later among the works it cites.
Adtr: Anomaly detection transformer with feature reconstruction
Zhiyuan You, Kai Yang, Wenhan Luo, Lei Cui, Yu Zheng, and Xinyi Le · 2022
Later among the works it cites.
Nice-slam: Neural implicit scalable encoding for slam
Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R Oswald, and Marc Pollefeys · 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
Later among the works it cites.
Complementary pseudo multimodal feature for point cloud anomaly detection
Yunkang Cao, Xiaohao Xu, and Weiming Shen · 2023
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Xiaoxue Chen, Junchen Liu, Hao Zhao, Guyue Zhou, and Ya-Qin Zhang · 2023
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Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection
Eliahu Horwitz and Yedid Hoshen · 2023
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Deep industrial image anomaly detection: A survey
Jiaqi Liu, Guoyang Xie, Jingbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin · 2023
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Asymmetric student-teacher networks for industrial anomaly detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2023
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Unsupervised road anomaly detection with language anchors
Beiwen Tian, Mingdao Liu, Huan-ang Gao, Pengfei Li, Hao Zhao, and Guyue Zhou · 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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Mars: An instance-aware, modular and realistic simulator for autonomous driving
Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuantao Chen, Runyi Yang, et al · 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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Memseg: A semi-supervised method for image surface defect detection using differences and commonalities
Minghui Yang, Peng Wu, and Hui Feng · 2023
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Latitude: Robotic global localization with truncated dynamic low-pass filter in city-scale nerf
Zhenxin Zhu, Yuantao Chen, Zirui Wu, Chao Hou, Yongliang Shi, Chuxuan Li, Pengfei Li, Hao Zhao, and Guyue Zhou · 2023
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