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We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models.
ImageNet classification with deep convolutional neural networks
Geoffrey E Hinton, Alex Krizhevsky, and Ilya Sutskever · 2012
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Surface defect saliency of magnetic tile
Yibin Huang, Congying Qiu, Yue Guo, Xiaonan Wang, and Kui Yuan · 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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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
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Encoding structure-texture relation with p-net for anomaly detection in retinal images
Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, and Shenghua Gao · 2020
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Deep generative model using unregularized score for anomaly detection with heterogeneous complexity
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Patch SVDD: Patch-level SVDD for anomaly detection and segmentation
Jihun Yi and Sungroh Yoon · 2020
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Learning and evaluating representations for deep one-class classification
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Anomaly detection based on zero-shot outlier synthesis and hierarchical feature distillation
Adín Ramírez Rivera, Adil Khan, Imad Eddine Ibrahim Bekkouch, and Taimoor Shakeel Sheikh · 2020
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Industrial image anomaly localization based on gaussian clustering of pretrained feature
Qian Wan, Liang Gao, Xinyu Li, and Long Wen · 2021
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Autoencoders for unsupervised anomaly segmentation in brain mr images: a comparative study
Christoph Baur, Stefan Denner, Benedikt Wiestler, Nassir Navab, and Shadi Albarqouni · 2021
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Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection
Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, and Hong Zhou · 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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Learning semantic context from normal samples for unsupervised anomaly detection
Xudong Yan, Huaidong Zhang, Xuemiao Xu, Xiaowei Hu, and Pheng-Ann Heng · 2021
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Mocca: Multilayer one-class classification for anomaly detection
Fabio Valerio Massoli, Fabrizio Falchi, Alperen Kantarci, Şeymanur Akti, Hazim Kemal Ekenel, and Giuseppe Amato · 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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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Multiresolution knowledge distillation for anomaly detection
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H Rohban, and Hamid R Rabiee · 2021
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Student-teacher feature pyramid matching for anomaly detection
Guodong Wang, Shumin Han, Errui Ding, and Di Huang · 2021
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Zero-shot anomalous object detection using unsupervised metric learning
Jiahui Liu, Xiaojuan Qi, Songzhi Su, Tony Prescott, and Li Sun · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Christoph Schuhmann, Robert Kaczmarczyk, Aran Komatsuzaki, Aarush Katta, Richard Vencu, Romain Beaumont, Jenia Jitsev, Theo Coombes, and Clayton Mullis · 2021
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Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi · 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, Jakob Uszkoreit, and Neil Houlsby · 2021
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Mixed supervision for surface-defect detection: From weakly to fully supervised learning
Jakob Božič, Domen Tabernik, and Danijel Skočaj · 2021
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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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Regionclip: Region-based language-image pretraining
Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al · 2022
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Extract free dense labels from clip
Chong Zhou, Chen Change Loy, and Bo Dai · 2022
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Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Prompting visual-language models for efficient video understanding
Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie · 2022
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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Masked swin transformer unet for industrial anomaly detection
Jielin Jiang, Jiale Zhu, Muhammad Bilal, Yan Cui, Neeraj Kumar, Ruihan Dou, Feng Su, and Xiaolong Xu · 2022
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SoftPatch: Unsupervised anomaly detection with noisy data
Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, Kai Wu, Yong Liu, Chengjie Wang, and Feng Zheng · 2022
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Align and prompt: Video-and-language pre-training with entity prompts
Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, and Steven CH Hoi · 2022
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Image segmentation using text and image prompts
Timo Lüddecke and Alexander Ecker · 2022
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Towards robust video object segmentation with adaptive object calibration
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Reliable propagation-correction modulation for video object segmentation
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Rˆ 2vos: Robust referring video object segmentation via relational multimodal cycle consistency
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Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola · 2022
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Unified vision and language prompt learning
Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy · 2022
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Multitask vision-language prompt tuning
Sheng Shen, Shijia Yang, Tianjun Zhang, Bohan Zhai, Joseph E Gonzalez, Kurt Keutzer, and Trevor Darrell · 2022
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Grounded language-image pre-training
Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al · 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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Collaborative discrepancy optimization for reliable image anomaly localization
Yunkang Cao, Xiaohao Xu, Zhaoge Liu, and Weiming Shen · 2023
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Complementary pseudo multimodal feature for point cloud anomaly detection
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Winclip: Zero-/few-shot anomaly classification and segmentation
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What does clip know about a red circle? visual prompt engineering for vlms
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