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To improve logical anomaly detection, some previous works have integrated segmentation techniques with conventional anomaly detection methods.
A density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al · 1996
Earlier work this paper cites.
Mean shift: A robust approach toward feature space analysis
Dorin Comaniciu and Peter Meer · 2002
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Density-based clustering based on hierarchical density estimates
Ricardo JGB Campello, Davoud Moulavi, and Jörg Sander · 2013
Earlier work this paper cites.
V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Earlier work this paper cites.
Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
Earlier work this paper cites.
Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
Earlier work this paper cites.
Sub-image anomaly detection with deep pyramid correspondences
Niv Cohen and Yedid Hoshen · 2020
Earlier work this paper cites.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Earlier work this paper cites.
Padim: a patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 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.
Glancing at the patch: Anomaly localization with global and local feature comparison
Shenzhi Wang, Liwei Wu, Lei Cui, and Yujun Shen · 2021
Cited alongside, same era.
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2021
Cited alongside, same era.
Anomalib: A deep learning library for anomaly detection
Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, and Utku Genc · 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.
Open-set image tagging with multi-grained text supervision
Xinyu Huang, Yi-Jie Huang, Youcai Zhang, Weiwei Tian, Rui Feng, Yuejie Zhang, Yanchun Xie, Yaqian Li, and Lei Zhang · 2023
Later among the works it cites.
Segment anything in high quality
Lei Ke, Mingqiao Ye, Martin Danelljan, Yifan Liu, Yu-Wing Tai, Chi-Keung Tang, and Fisher Yu · 2023
Later among the works it cites.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
Later among the works it cites.
Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
Tongkun Liu, Bing Li, Xiao Du, Bingke Jiang, Xiao Jin, Liuyi Jin, and Zhuo Zhao · 2023
Later among the works it cites.
Inter-realization channels: Unsupervised anomaly detection beyond one-class classification
Declan McIntosh and Alexandra Branzan Albu · 2023
Later among the works it cites.
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Hanqiu Deng and Xingyu Li · 2022
Cited alongside, same era.
Registration based few-shot anomaly detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratlin, and Yanfeng Wang · 2022
Cited alongside, same era.
Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2022
Cited alongside, same era.
Semi-supervised semantic segmentation using unreliable pseudo-labels
Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le · 2022
Cited alongside, same era.
Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer · 2022
Cited alongside, same era.
Pni: industrial anomaly detection using position and neighborhood information
Jaehyeok Bae, Jae-Han Lee, and Seyun Kim · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Asymmetric student-teacher networks for industrial anomaly detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and Bastian Wandt · 2023
Later among the works it cites.
Revisiting reverse distillation for anomaly detection
Tran Dinh Tien, Anh Tuan Nguyen, Nguyen Hoang Tran, Ta Duc Huy, Soan Duong, Chanh D Tr Nguyen, and Steven QH Truong · 2023
Later among the works it cites.
Recognize anything: A strong image tagging model
Youcai Zhang, Xinyu Huang, Jinyu Ma, Zhaoyang Li, Zhaochuan Luo, Yanchun Xie, Yuzhuo Qin, Tong Luo, Yaqian Li, Shilong Liu, et al · 2023
Later among the works it cites.
Efficientad: Accurate visual anomaly detection at millisecond-level latencies
Kilian Batzner, Lars Heckler, and Rebecca König · 2024
Closest in time.
Reconpatch: Contrastive patch representation learning for industrial anomaly detection
Jeeho Hyun, Sangyun Kim, Giyoung Jeon, Seung Hwan Kim, Kyunghoon Bae, and Byung Jun Kang · 2024
Closest in time.
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 Sang Hyun Park · 2024
Closest in time.
Grounded sam: Assembling open-world models for diverse visual tasks, 2024
Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang · 2024
Closest in time.
Contextual affinity distillation for image anomaly detection
Jie Zhang, Masanori Suganuma, and Takayuki Okatani · 2024
Closest in time.