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Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection.
Outlier detection for high dimensional data
Charu C Aggarwal and Philip S Yu · 2001
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A survey of outlier detection methodologies
Victoria Hodge and Jim Austin · 2004
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Outlier detection
Irad Ben-Gal · 2005
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80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
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Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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Review of novelty detection methods
Dubravko Miljković · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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A review of novelty detection
Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Discovering states and transformations in image collections
Phillip Isola, Joseph J Lim, and Edward H Adelson · 2015
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Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection
Deegan J Atha and Mohammad R Jahanshahi · 2018
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Head ct - hemorrhage
Kitamura Felipe · 2018
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Accident detection from cctv footage
C. Kay · 2018
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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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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Detecting multivariate outliers: Use a robust variant of the mahalanobis distance
Christophe Leys, Olivier Klein, Yves Dominicy, and Christophe Ley · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Real-world anomaly detection in surveillance videos
Waqas Sultani, Chen Chen, and Mubarak Shah · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Iterative learning with open-set noisy labels
Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia · 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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Learning and the unknown: Surveying steps toward open world recognition
Terrance E Boult, Steve Cruz, Akshay Raj Dhamija, M Gunther, James Henrydoss, and Walter J Scheirer · 2019
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Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 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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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Detecting out-of-distribution examples with in-distribution examples and gram matrices
Chandramouli Shama Sastry and Sageev Oore · 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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Anomalous example detection in deep learning: A survey
Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K Varshney, and Dawn Song · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Neural batch sampling with reinforcement learning for semi-supervised anomaly detection
Wen-Hsuan Chu and Kris M Kitani · 2020
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Recent advances in open set recognition: A survey
Chuanxing Geng, Sheng-jun Huang, and Songcan Chen · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John D Owens, and Yixuan Li · 2020
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Detecting out-of-distribution examples with gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2020
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A survey on deep learning techniques for video anomaly detection
Jessie James P Suarez and Prospero C Naval Jr · 2020
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Segmentation-based deep-learning approach for surface-defect detection
Domen Tabernik, Samo Šela, Jure Skvarč, and Danijel Skočaj · 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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Multi-task curriculum framework for open-set semi-supervised learning
Qing Yu, Daiki Ikami, Go Irie, and Kiyoharu Aizawa · 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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Padim: a patch distribution modeling framework for anomaly detection and localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 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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Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan · 2021
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Mos: Towards scaling out-of-distribution detection for large semantic space
Rui Huang and Yixuan Li · 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
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Joint representation learning and novel category discovery on single-and multi-modal data
Xuhui Jia, Kai Han, Yukun Zhu, and Bradley Green · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Cutpaste: Self-supervised learning for anomaly detection and localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister · 2021
Cited alongside, same era.
Mood: Multi-level out-of-distribution detection
Ziqian Lin, Sreya Dutta Roy, and Yixuan Li · 2021
Cited alongside, same era.
Explainable deep one-class classification
Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Marius Kloft, and Klaus-Robert Müller · 2021
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A survey on open set recognition
Atefeh Mahdavi and Marco Carvalho · 2021
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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
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Random word data augmentation with clip for zero-shot anomaly detection
Masato Tamura · 2023
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Pouf: Prompt-oriented unsupervised fine-tuning for large pre-trained models
Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He, and Mingyuan Zhou · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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A closer look at the robustness of contrastive language-image pre-training (clip)
Weijie Tu, Weijian Deng, and Tom Gedeon · 2023
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Sus-x: Training-free name-only transfer of vision-language models
Vishaal Udandarao, Ankush Gupta, and Samuel Albanie · 2023
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Sina Mohseni, Haotao Wang, Zhiding Yu, Chaowei Xiao, Zhangyang Wang, and Jay Yadawa · 2021
Cited alongside, same era.
Deep learning for anomaly detection: A review
Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
Cited alongside, same era.
A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R Kauffmann, Robert A Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G Dietterich, and Klaus-Robert Müller · 2021
Cited alongside, same era.
Openmatch: Open-set semi-supervised learning with open-set consistency regularization
Kuniaki Saito, Donghyun Kim, and Kate Saenko · 2021
Cited alongside, same era.
An effective baseline for robustness to distributional shift
Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, and Jeff Bilmes · 2021
Cited alongside, same era.
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al · 2023
Later among the works it cites.
Full-spectrum out-of-distribution detection
Jingkang Yang, Kaiyang Zhou, and Ziwei Liu · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
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Autolabel: Clip-based framework for open-set video domain adaptation
Giacomo Zara, Subhankar Roy, Paolo Rota, and Elisa Ricci · 2023
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Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
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Out-of-distribution detection based on in-distribution data patterns memorization with modern hopfield energy
Jinsong Zhang, Qiang Fu, Xu Chen, Lun Du, Zelin Li, Gang Wang, Shi Han, Dongmei Zhang, et al · 2023
Later among the works it cites.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Later among the works it cites.
Video anomaly detection in 10 years: A survey and outlook
Moshira Abdalla, Sajid Javed, Muaz Al Radi, Anwaar Ulhaq, and Naoufel Werghi · 2024
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Adapting contrastive language-image pretrained (clip) models for out-of-distribution detection
Nikolas Adaloglou, Felix Michels, Tim Kaiser, and Markus Kollmann · 2024
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Cableinspect-AD: An expert-annotated anomaly detection dataset
Akshatha Arodi, Margaux Luck, Jean-Luc Bedwani, Aldo Zaimi, Ge Li, Nicolas Pouliot, Julien Beaudry, and Gaétan Marceau Caron · 2024
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Unexplored faces of robustness and out-of-distribution: Covariate shifts in environment and sensor domains
Eunsu Baek, Keondo Park, Jiyoon Kim, and Hyung-Sin Kim · 2024
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Zero-shot out-of-distribution detection with outlier label exposure
Choubo Ding and Guansong Pang · 2024
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Uncovering what why and how: A comprehensive benchmark for causation understanding of video anomaly
Hang Du, Sicheng Zhang, Binzhu Xie, Guoshun Nan, Jiayang Zhang, Junrui Xu, Hangyu Liu, Sicong Leng, Jiangming Liu, Hehe Fan, et al · 2024
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Long-tailed anomaly detection with learnable class names
Chih-Hui Ho, Kuan-Chuan Peng, and Nuno Vasconcelos · 2024
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Out-of-distribution detection in medical image analysis: A survey
Zesheng Hong, Yubiao Yue, Yubin Chen, Lele Cong, Huanjie Lin, Yuanmei Luo, Mini Han Wang, Weidong Wang, Jialong Xu, Xiaoqi Yang, et al · 2024
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Medicalclip: Anomaly-detection domain generalization with asymmetric constraints
Liujie Hua, Yueyi Luo, Qianqian Qi, and Jun Long · 2024
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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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Negative label guided ood detection with pretrained vision-language models
Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, and Bo Han · 2024
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Enhancing near ood detection in prompt learning: Maximum gains, minimal costs
Myong Chol Jung, He Zhao, Joanna Dipnall, Belinda Gabbe, and Lan Du · 2024
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Few-shot anomaly detection via personalization
Sangkyung Kwak, Jongheon Jeong, Hankook Lee, Woohyuck Kim, Dongho Seo, Woojin Yun, Wonjin Lee, and Jinwoo Shin · 2024
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Gallop: Learning global and local prompts for vision-language models
Marc Lafon, Elias Ramzi, Clément Rambour, Nicolas Audebert, and Nicolas Thome · 2024
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Realistic unsupervised CLIP fine-tuning with universal entropy optimization
Jian Liang, Lijun Sheng, Zhengbo Wang, Ran He, and Tieniu Tan · 2024
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Vila: On pre-training for visual language models
Ji Lin, Hongxu Yin, Wei Ping, Yao Lu, Pavlo Molchanov, Andrew Tao, Huizi Mao, Jan Kautz, Mohammad Shoeybi, and Song Han · 2024
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Tag: Text prompt augmentation for zero-shot out-of-distribution detection
Xixi Liu and Zach Christopher · 2024
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Open-set recognition in the age of vision-language models
Dimity Miller, Niko Sünderhauf, Alex Kenna, and Keita Mason · 2024
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Vcp-clip: A visual context prompting model for zero-shot anomaly segmentation
Zhen Qu, Xian Tao, Mukesh Prasad, Fei Shen, Zhengtao Zhang, Xinyi Gong, and Guiguang Ding · 2024
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Simple outlier detection for time series
Stack Exchange User · 2024
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Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2024
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Self-calibrated tuning of vision-language models for out-of-distribution detection
Geng Yu, Jianing Zhu, Jiangchao Yao, and Bo Han · 2024
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Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al · 2024
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Adaneg: Adaptive negative proxy guided ood detection with vision-language models
Yabin Zhang and Lei Zhang · 2024
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Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts
Jiawen Zhu and Guansong Pang · 2024
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Clip-fsac: Boosting clip for few-shot anomaly classification with synthetic anomalies
Zuo Zuo, Yao Wu, Baoqiang Li, Jiahao Dong, You Zhou, Lei Zhou, Yanyun Qu, and Zongze Wu · 2024
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Manta: A large-scale multi-view and visual-text anomaly detection dataset for tiny objects
Lei Fan, Dongdong Fan, Zhiguang Hu, Yiwen Ding, Donglin Di, Kai Yi, Maurice Pagnucco, and Yang Song · 2025
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Univad: A training-free unified model for few-shot visual anomaly detection
Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, and Jinqiao Wang · 2025
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Mmad: The first-ever comprehensive benchmark for multimodal large language models in industrial anomaly detection
Xi Jiang, Jian Li, Hanqiu Deng, Yong Liu, Bin-Bin Gao, Yifeng Zhou, Jialin Li, Chengjie Wang, and Feng Zheng · 2025
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Clipsam: Clip and sam collaboration for zero-shot anomaly segmentation
Shengze Li, Jianjian Cao, Peng Ye, Yuhan Ding, Chongjun Tu, and Tao Chen · 2025
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Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip
Wenxin Ma, Xu Zhang, Qingsong Yao, Fenghe Tang, Chenxu Wu, Yingtai Li, Rui Yan, Zihang Jiang, and S Kevin Zhou · 2025
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A benchmark and evaluation for real-world out-of-distribution detection using vision-language models
Shiho Noda, Atsuyuki Miyai, Qing Yu, Go Irie, and Kiyoharu Aizawa · 2025
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Bayesian prompt flow learning for zero-shot anomaly detection
Zhen Qu, Xian Tao, Xinyi Gong, Shichen Qu, Qiyu Chen, Zhengtao Zhang, Xingang Wang, and Guiguang Ding · 2025
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Cut: A controllable, universal, and training-free visual anomaly generation framework
Han Sun, Yunkang Cao, and Olga Fink · 2025
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Muirbench: A comprehensive benchmark for robust multi-image understanding
Fei Wang, Xingyu Fu, James Y Huang, Zekun Li, Qin Liu, Xiaogeng Liu, Mingyu Derek Ma, Nan Xu, Wenxuan Zhou, Kai Zhang, et al · 2025
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Large language models for anomaly and out-of-distribution detection: A survey
Ruiyao Xu and Kaize Ding · 2025
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Promptable anomaly segmentation with sam through self-perception tuning
Hui-Yue Yang, Hui Chen, Ao Wang, Kai Chen, Zijia Lin, Yongliang Tang, Pengcheng Gao, Yuming Quan, Jungong Han, and Guiguang Ding · 2025
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Vera: Explainable video anomaly detection via verbalized learning of vision-language models
Muchao Ye, Weiyang Liu, and Pan He · 2025
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Language-assisted feature transformation for anomaly detection
EungGu Yun, Heonjin Ha, Yeongwoo Nam, and Bryan Dongik Lee · 2025
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Local-prompt: Extensible local prompts for few-shot out-of-distribution detection
Fanhu Zeng, Zhen Cheng, Fei Zhu, Hongxin Wei, and Xu-Yao Zhang · 2025
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