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Visual Anomaly Detection (VAD) aims to identify abnormal samples in images that deviate from normal patterns, covering multiple domains, including industrial, logical, and medical fields.
Anomalygpt: Detecting industrial anomalies using large vision-language models
Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, and Jinqiao Wang · 1940
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Bennett Landman, Zhoubing Xu, J Igelsias, Martin Styner, Thomas Langerak, and Arno Klein · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
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Identifying medical diagnoses and treatable diseases by image-based deep learning
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al · 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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Automated segmentation of macular edema in oct using deep neural networks
Junjie Hu, Yuanyuan Chen, and Zhang Yi · 2019
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Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C Kitamura, Sarthak Pati, et al · 2021
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Madgan: Unsupervised medical anomaly detection gan using multiple adjacent brain mri slice reconstruction
Changhee Han, Leonardo Rundo, Kohei Murao, Tomoyuki Noguchi, Yuki Shimahara, Zoltán Ádám Milacski, Saori Koshino, Evis Sala, Hideki Nakayama, and Shin’ichi Satoh · 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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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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Catching both gray and black swans: Open-set supervised anomaly detection
Choubo Ding, Guansong Pang, and Chunhua Shen · 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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Medclip: Contrastive learning from unpaired medical images and text
Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, and Jimeng Sun · 2022
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A unified model for multi-class anomaly detection
Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le · 2022
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Agca: An adaptive graph channel attention module for steel surface defect detection
Xin Xiang, Zenghui Wang, Jun Zhang, Yi Xia, Peng Chen, and Bing Wang · 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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Bmad: Benchmarks for medical anomaly detection
Jinan Bao, Hanshi Sun, Hanqiu Deng, Yinsheng He, Zhaoxiang Zhang, and Xingyu Li · 2024
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Rethinking autoencoders for medical anomaly detection from a theoretical perspective
Yu Cai, Hao Chen, and Kwang-Ting Cheng · 2024
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Csad: Unsupervised component segmentation for logical anomaly detection
Yu-Hsuan Hsieh and Shang-Hong Lai · 2024
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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
Cited alongside, same era.
The liver tumor segmentation benchmark (lits)
Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, et al · 2023
Cited alongside, same era.
Xuhai Chen, Yue Han, and Jiangning Zhang · 2023
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
Cited alongside, same era.
Winclip: Zero-/few-shot anomaly classification and segmentation
Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer · 2023
Cited alongside, same era.
Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Cited alongside, same era.
Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images
Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W Verjans, Rajvinder Singh, and Gustavo Carneiro · 2023
Cited alongside, same era.
Adapting visual-language models for generalizable anomaly detection in medical images
Chaoqin Huang, Aofan Jiang, Jinghao Feng, Ya Zhang, Xinchao Wang, and Yanfeng Wang · 2024
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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
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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 Sang Hyun Park · 2024
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Sam-lad: Segment anything model meets zero-shot logic anomaly detection
Yun Peng, Xiao Lin, Nachuan Ma, Jiayuan Du, Chuangwei Liu, Chengju Liu, and Qijun Chen · 2024
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Grounded sam: Assembling open-world models for diverse visual tasks
Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, et al · 2024
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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 · 2024
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