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Recently, multi-class anomaly classification has garnered increasing attention.
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A threshold selection method from gray-level histograms
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An algorithm for fast adaptive image binarization with applications in radiotherapy imaging
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Robust document image binarization technique for degraded document images
Bolan Su, Shijian Lu, and Chew Lim Tan · 2012
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Performance evaluation methodology for historical document image binarization
Konstantinos Ntirogiannis, Basilis Gatos, and Ioannis Pratikakis · 2013
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Deepedge: A multi-scale bifurcated deep network for top-down contour detection
Gedas Bertasius, Jianbo Shi, and Lorenzo Torresani · 2015
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Learning to cluster in order to transfer across domains and tasks
Yen-Chang Hsu, Zhaoyang Lv, and Zsolt Kira · 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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GiB: A Game Theory Inspired Binarization Technique for Degraded Document Images
Showmik Bhowmik, Ram Sarkar, Bishwadeep Das, and David Doermann · 2019
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Learning to discover novel visual categories via deep transfer clustering
Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2019
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Multi-class classification without multi-class labels
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, and Zsolt Kira · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, Joao F Henriques, and Andrea Vedaldi · 2019
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A novel multi-layer framework for tiny obstacle discovery
Feng Xue, Anlong Ming, and Yu Zhou · 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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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 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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Surface defect saliency of magnetic tile
Yibin Huang, Congying Qiu, and Kui Yuan · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Gatcluster: Self-supervised gaussian-attention network for image clustering
Chuang Niu, Jun Zhang, Ge Wang, and Jimin Liang · 2020
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Scan: Learning to classify images without labels
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 2020
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Tiny obstacle discovery by occlusion-aware multilayer regression
Feng Xue, Anlong Ming, and Yu Zhou · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Class-relation knowledge distillation for novel class discovery
Peiyan Gu, Chuyu Zhang, Ruijie Xu, and Xuming He · 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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Uniformaly: Towards task-agnostic unified framework for visual anomaly detection
Yujin Lee, Harin Lim, Seoyoon Jang, and Hyunsoo Yoon · 2023
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Omni-frequency channel-selection representations for unsupervised anomaly detection
Yufei Liang, Jiangning Zhang, Shiwei Zhao, Runze Wu, Yong Liu, and Shuwen Pan · 2023
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Novel class discovery for 3d point cloud semantic segmentation
Luigi Riz, Cristiano Saltori, Elisa Ricci, and Fabio Poiesi · 2023
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Yibin Huang, Congying Qiu, and Kui Yuan · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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A unified objective for novel class discovery
Enrico Fini, Enver Sangineto, Stéphane Lathuiliere, Zhun Zhong, Moin Nabi, and Elisa Ricci · 2021
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Autonovel: Automatically discovering and learning novel visual categories
Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Andrea Vedaldi, and Andrew Zisserman · 2021
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A lightweight spatial and temporal multi-feature fusion network for defect detection
Bozhen Hu, Bin Gao, Wai Lok Woo, Lingfeng Ruan, Jikun Jin, Yang Yang, and Yongjie Yu · 2021
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Reference-based defect detection network
Zhaoyang Zeng, Bei Liu, Jianlong Fu, and Hongyang Chao · 2021
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Anomaly clustering: Grouping images into coherent clusters of anomaly types
Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, and Tomas Pfister · 2023
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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
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No representation rules them all in category discovery
Sagar Vaze, Andrea Vedaldi, and Andrew Zisserman · 2023
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Parametric classification for generalized category discovery: A baseline study
Xin Wen, Bingchen Zhao, and Xiaojuan Qi · 2023
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Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffusion models
Weijia Wu, Yuzhong Zhao, Mike Zheng Shou, Hong Zhou, and Chunhua Shen · 2023
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Industrial anomaly detection with domain shift: A real-world dataset and masked multi-scale reconstruction
Zilong Zhang, Zhibin Zhao, Xingwu Zhang, Chuang Sun, and Xuefeng Chen · 2023
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Occlusion relationship reasoning with a feature separation and interaction network
Yu Zhou, Rui Lu, Feng Xue, and Yuzhe Gao · 2023
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Pni: industrial anomaly detection using position and neighborhood information
Jaehyeok Bae, Jae-Han Lee, and Seyun Kim · 2023
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Uniformaly: Towards task-agnostic unified framework for visual anomaly detection
Yujin Lee, Harin Lim, Seoyoon Jang, and Hyunsoo Yoon · 2023
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Anomaly clustering: Grouping images into coherent clusters of anomaly types
Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, and Tomas Pfister · 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
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Bootstrap your own prior: Towards distribution-agnostic novel class discovery
Muli Yang, Liancheng Wang, Cheng Deng, and Hanwang Zhang · 2023
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Blind localization and clustering of anomalies in textures
Andrei-Timotei Ardelean and Tim Weyrich · 2024
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Amend: Adaptive margin and expanded neighborhood for efficient generalized category discovery
Anwesha Banerjee, Liyana Sahir Kallooriyakath, and Soma Biswas · 2024
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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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Seeing unseen: Discover novel biomedical concepts via geometry-constrained probabilistic modeling
Jianan Fan, Dongnan Liu, Hang Chang, Heng Huang, Mei Chen, and Weidong Cai · 2024
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Indoor obstacle discovery on reflective ground via monocular camera
Feng Xue, Yicong Chang, Tianxi Wang, Yu Zhou, and Anlong Ming · 2024
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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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Dual-level adaptive self-labeling for novel class discovery in point cloud segmentation
Ruijie Xu, Chuyu Zhang, Hui Ren, and Xuming He · 2025
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