{"type":"ScholarlyArticle","title":"CLIP-FSAC: Boosting CLIP for Few-Shot Anomaly Classification with Synthetic Anomalies","authors":[{"name":"Zuo Zuo","slug":"zuo-zuo","given":"Zuo","family":"Zuo","affiliation":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, and Institute of Artificial Intelligence and Robotics , Xi'an Jiaotong University; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)"},{"name":"Yao Wu","slug":"yao-wu","given":"Yao","family":"Wu","affiliation":"Xiamen University"},{"name":"Baoqiang Li","slug":"baoqiang-li","given":"Baoqiang","family":"Li","affiliation":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)"},{"name":"Jiahao Dong","slug":"jiahao-dong","given":"Jiahao","family":"Dong","affiliation":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)"},{"name":"You Zhou","slug":"you-zhou","given":"You","family":"Zhou","affiliation":"Shenzhen University"},{"name":"Lei Zhou","slug":"lei-zhou","given":"Lei","family":"Zhou","affiliation":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)"},{"name":"Yanyun Qu","slug":"yanyun-qu","given":"Yanyun","family":"Qu","affiliation":"Xiamen University"},{"name":"Zongze Wu","slug":"zongze-wu","given":"Zongze","family":"Wu","affiliation":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, and Institute of Artificial Intelligence and Robotics , Xi'an Jiaotong University; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ); Shenzhen University"}],"abstract":"Few-shot anomaly classification (FSAC) is a vital task in manufacturing industry. Recent methods focus on utilizing CLIP in zero/few normal shot anomaly detection instead of custom models. However, there is a lack of specific text prompts in anomaly classification and most of them ignore the modality gap between image and text. Meanwhile, there is distribution discrepancy between the pre-trained and the target data. To provide a remedy, in this paper, we propose a method to boost CLIP for few-normal-shot anomaly classification, dubbed CLIP-FSAC, which contains two-stage of training and alternating fine-tuning with two modality-specific adapters. Specifically, in the first stage, we train image adapter with text representation output from text encoder and introduce an image-to-text tuning to enhance multi-modal interaction and facilitate a better language-compatible visual representation. In the second stage, we freeze the image adapter to train the text adapter. Both of them are constrained by fusion-text contrastive loss. Comprehensive experiment results are provided for evaluating our method in few-normal-shot anomaly classification, which outperforms the state-of-the-art method by 12.2%, 10.9%, 10.4% AUROC on VisA for 1, 2, and 4-shot settings.","doi":"10.24963/ijcai.2024/203","url":"https://ijcai.org/proceedings/2024/203","pdf":"https://ijcai.org/proceedings/2024/0203.pdf","markdown":"https://ijcai.org/proceedings/2024/203.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/203","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":203,"pages":{"first":1834,"last":1842},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Multimodal learning"],"videos":[],"references":[{"position":1,"raw":"[Akcay et al., 2018] Samet Akcay, Amir Atapour- Abarghouei, and Toby P. Breckon. Ganomaly: Semi- supervised anomaly detection via adversarial training. In ACCV, pages 622-637, 2018.","title":"Ganomaly: Semisupervised anomaly detection via adversarial training","authors":"Samet Akcay, Toby P Amir Atapour-Abarghouei, Breckon","year":"2018","venue":"ACCV","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Alayrac et al., 2022] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob L Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikoł aj Bińkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zis- serman, and Karén Simonyan. Flamingo: a visual lan- guage model for few-shot learning. In NIPS, pages 23716- 23736, 2022.","title":"Flamingo: a visual language model for few-shot learning","authors":"Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob L Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Ricardo Mikoł Aj Bińkowski, Oriol Barreira, Andrew Vinyals, Karén Zisserman, Simonyan","year":"2022","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bae et al., 2023] Jaehyeok Bae, Jae-Han Lee, and Seyun Kim. Pni : Industrial anomaly detection using position and neighborhood information. In ICCV, pages 6373-6383, 2023.","title":"Pni : Industrial anomaly detection using position and neighborhood information","authors":"Bae","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Batzner et al., 2024] Kilian Batzner, Lars Heckler, and Re- becca König. Efficientad: Accurate visual anomaly detec- tion at millisecond-level latencies. In WACV, pages 128- 138, 2024.","title":"Efficientad: Accurate visual anomaly detection at millisecond-level latencies","authors":"Kilian Batzner, Lars Heckler, Rebecca König","year":"2024","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bergmann et al., 2019] Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. Mvtec ad -a com- prehensive real-world dataset for unsupervised anomaly detection. In CVPR, pages 9584-9592, 2019.","title":"Mvtec ad -a comprehensive real-world dataset for unsupervised anomaly detection","authors":"Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cao et al., 2023] Yunkang Cao, Xiaohao Xu, Zhaoge Liu, and Weiming Shen. Collaborative discrepancy opti- mization for reliable image anomaly localization. TII, 19(11):10674-10683, 2023.","title":"Collaborative discrepancy optimization for reliable image anomaly localization","authors":"Cao","year":"2023","venue":"TII","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2020] Ting Chen, Simon Kornblith, Moham- mad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In ICML, ICML'20, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton","year":"2020","venue":"ICML, ICML'20","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Defard et al., 2021] Thomas Defard, Aleksandr Setkov, An- gelique Loesch, and Romaric Audigier. Padim: A patch distribution modeling framework for anomaly detection and localization. In ICPRW, page 475-489, 2021.","title":"Padim: A patch distribution modeling framework for anomaly detection and localization","authors":"Thomas Defard, Aleksandr Setkov, Angelique Loesch, Romaric Audigier","year":"2021","venue":"ICPRW","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fang et al., 2023] Zheng Fang, Xiaoyang Wang, Haocheng Li, Jiejie Liu, Qiugui Hu, and Jimin Xiao. Fastrecon: Few- shot industrial anomaly detection via fast feature recon- struction. In ICCV, pages 17481-17490, 2023.","title":"Fastrecon: Fewshot industrial anomaly detection via fast feature reconstruction","authors":"Zheng Fang, Xiaoyang Wang, Haocheng Li, Jiejie Liu, Qiugui Hu, Jimin Xiao","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gao et al., 2021] Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao. Clip-adapter: Better vision-language models with feature adapters. arXiv, 2021.","title":"Clip-adapter: Better vision-language models with feature adapters","authors":"Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, Yu Qiao","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Guo et al., 2023] Hewei Guo, Liping Ren, Jingjing Fu, Yuwang Wang, Zhizheng Zhang, Cuiling Lan, Haoqian Wang, and Xinwen Hou. Template-guided hierarchical feature restoration for anomaly detection. In ICCV, pages 6447-6458, 2023.","title":"Template-guided hierarchical feature restoration for anomaly detection","authors":"Guo","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[He et al., 2023] Haoyang He, Jiangning Zhang, Hongxu Chen, Xuhai Chen, Zhishan Li, Xu Chen, Yabiao Wang, Chengjie Wang, and Lei Xie. Diad: A diffusion-based framework for multi-class anomaly detection. arXiv, 2023.","title":"Diad: A diffusion-based framework for multi-class anomaly detection","authors":"He","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2022] Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, and Yan-Feng Wang. Registration based few-shot anomaly detection. In ECCV, page 303-319, 2022.","title":"Registration based few-shot anomaly detection","authors":"Huang","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jeong et al., 2023] Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer. Winclip: Zero-/few-shot anomaly classification and segmentation. In CVPR, pages 19606-19616, 2023.","title":"Winclip: Zero-/few-shot anomaly classification and segmentation","authors":"Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, Onkar Dabeer","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jia et al., 2021] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text su- pervision. In ICML, pages 4904-4916, 2021.","title":"Scaling up visual and vision-language representation learning with noisy text supervision","authors":"Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, Tom Duerig","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2021] Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister. Cutpaste: Self-supervised learn- ing for anomaly detection and localization. In CVPR, pages 9659-9669, 2021.","title":"Cutpaste: Self-supervised learning for anomaly detection and localization","authors":"Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2023] Siyuan Li, Li Sun, and Qingli Li. Clip- reid: Exploiting vision-language model for image re- identification without concrete text labels. In AAAI, pages 1405-1413, 2023.","title":"Clipreid: Exploiting vision-language model for image reidentification without concrete text labels","authors":"Siyuan Li, Li Sun, Qingli Li","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liang et al., 2023] Zhexin Liang, Chongyi Li, Shangchen Zhou, Ruicheng Feng, and Chen Change Loy. Iterative prompt learning for unsupervised backlit image enhance- ment. In ICCV, pages 8094-8103, 2023.","title":"Iterative prompt learning for unsupervised backlit image enhancement","authors":"Liang","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2022] Tongkun Liu, Bing Li, Zhuo Zhao, Xiao Du, Bingke Jiang, and Leqi Geng. Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection. arXiv, 2022.","title":"Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection","authors":"Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2023a] Tongkun Liu, Bing Li, Xiao Du, Bingke Jiang, Leqi Geng, Feiyang Wang, and Zhuo Zhao. Fair: Frequency-aware image restoration for industrial visual anomaly detection. arXiv, 2023.","title":"Fair: Frequency-aware image restoration for industrial visual anomaly detection","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2023b] Zhikang Liu, Yiming Zhou, Yuansheng Xu, and Zilei Wang. Simplenet: A simple network for im- age anomaly detection and localization. In CVPR, pages 20402-20411, 2023.","title":"Simplenet: A simple network for image anomaly detection and localization","authors":"Liu","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervi- sion. In ICML, pages 8748-8763, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Rao et al., 2022] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense pre- diction with context-aware prompting. In CVPR, pages 18061-18070, 2022.","title":"Denseclip: Language-guided dense prediction with context-aware prompting","authors":"Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, Jiwen Lu","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ristea et al., 2022] Nicolae-Cȃtȃlin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, and Mubarak Shah. Self- supervised predictive convolutional attentive block for anomaly detection. In CVPR, pages 13566-13576, 2022.","title":"Selfsupervised predictive convolutional attentive block for anomaly detection","authors":"Nicolae-Cȃtȃlin Ristea, Neelu Madan, Tudor Radu, Kamal Ionescu, Fahad Nasrollahi, Thomas B Shahbaz Khan, Mubarak Moeslund, Shah","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Roth et al., 2022] Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler. Towards total recall in industrial anomaly detec- tion. In CVPR, pages 14298-14308, 2022.","title":"Towards total recall in industrial anomaly detection","authors":"Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter Gehler","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Salehi et al., 2021] Mohammadreza Salehi, Niousha Sad- jadi, Soroosh Baselizadeh, Mohammad H. Rohban, and Hamid R. Rabiee. Multiresolution knowledge distillation for anomaly detection. In CVPR, pages 14897-14907, 2021.","title":"Multiresolution knowledge distillation for anomaly detection","authors":"Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H Rohban, Hamid R Rabiee","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Schlüter et al., 2022] Hannah M. Schlüter, Jeremy Tan, Benjamin Hou, and Bernhard Kainz. Natural synthetic anomalies for self-supervised anomaly detection and lo- calization. In ECCV, page 474-489, 2022.","title":"Natural synthetic anomalies for self-supervised anomaly detection and localization","authors":"M Hannah, Jeremy Schlüter, Benjamin Tan, Bernhard Hou, Kainz","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Schuhmann et al., 2021] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. LAION-400M: open dataset of clip- filtered 400 million image-text pairs. CoRR, 2021.","title":"LAION-400M: open dataset of clipfiltered 400 million image-text pairs","authors":"Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, Aran Komatsuzaki","year":"2021","venue":"CoRR","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Tien et al., 2023] Tran Dinh Tien, Anh Tuan Nguyen, Nguyen Hoang Tran, Ta Duc Huy, Soan T.M. Duong, Chanh D. Tr. Nguyen, and Steven Q. H. Truong. Revis- iting reverse distillation for anomaly detection. In CVPR, pages 24511-24520, 2023.","title":"Revisiting reverse distillation for anomaly detection","authors":"Tran Dinh, Tien, Anh Tuan Nguyen, Nguyen Hoang Tran, Ta Duc Huy, T M Soan, Duong, D Chanh, Tr, Steven Q H Nguyen, Truong","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2023] Henan","title":"Henan","authors":"Wang","year":"2023","venue":"Henan","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"Wang, Muli Yang, Kun Wei, and Cheng Deng. Hierarchical prompt learning for composi- tional zero-shot recognition. In IJCAI, pages 1470-1478, 2023.","title":"Hierarchical prompt learning for compositional zero-shot recognition","authors":"Muli Wang, Kun Yang, Cheng Wei, Deng","year":"2023","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2023/163"},{"position":33,"raw":"[Wu et al., 2023] Yao Wu, Mingwei Xing, Yachao Zhang, Yuan Xie, Zongze Wu, and Yanyun Qu. Perturbed pro- gressive learning for semisupervised defect segmentation. TNNLS, 2023.","title":"Perturbed progressive learning for semisupervised defect segmentation","authors":"Yao Wu, Mingwei Xing, Yachao Zhang, Yuan Xie, Zongze Wu, Yanyun Qu","year":"2023","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xie et al., 2023] Guoyang Xie, Jinbao Wang, Jiaqi Liu, Yaochu Jin, and Feng Zheng. Pushing the limits of few- shot anomaly detection in industry vision: Graphcore. In ICLR, 2023.","title":"Pushing the limits of fewshot anomaly detection in industry vision: Graphcore","authors":"Xie","year":"2023","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yao et al., 2022] Yuan Yao, Ao Zhang, Zhengyan Zhang, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. Cpt: Col- orful prompt tuning for pre-trained vision-language mod- els. arXiv, 2022.","title":"Cpt: Colorful prompt tuning for pre-trained vision-language models","authors":"Yuan Yao, Ao Zhang, Zhengyan Zhang, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yao et al., 2023] Xincheng Yao, Ruoqi Li, Zefeng Qian, Yan Luo, and Chongyang Zhang. Focus the discrepancy: Intra-and inter-correlation learning for image anomaly de- tection. In ICCV, pages 6803-6813, 2023.","title":"Focus the discrepancy: Intra-and inter-correlation learning for image anomaly detection","authors":"Yao","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yu et al., 2023] Wenwen Yu, Yuliang Liu, Wei Hua, De- qiang Jiang, Bo Ren, and Xiang Bai. Turning a clip model into a scene text detector. In CVPR, pages 6978-6988, 2023.","title":"Turning a clip model into a scene text detector","authors":"Wenwen Yu, Yuliang Liu, Wei Hua, Deqiang Jiang, Bo Ren, Xiang Bai","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zavrtanik et al., 2021] Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj. DrÆm -a discriminatively trained re- construction embedding for surface anomaly detection. In ICCV, pages 8310-8319, 2021.","title":"DrÆm -a discriminatively trained reconstruction embedding for surface anomaly detection","authors":"Zavrtanik","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2023a] Hui Zhang, Zheng Wang, Zuxuan Wu, and Yu-Gang Jiang. Diffusionad: Norm-guided one-step denoising diffusion for anomaly detection. arXiv, 2023.","title":"Diffusionad: Norm-guided one-step denoising diffusion for anomaly detection","authors":"Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2023b] Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, and Ting Chen. Destseg: Segmenta- tion guided denoising student-teacher for anomaly detec- tion. In CVPR, pages 3914-3923, 2023.","title":"Destseg: Segmentation guided denoising student-teacher for anomaly detection","authors":"Zhang","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhou et al., 2022] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. IJCV, page 2337-2348, sep 2022.","title":"Learning to prompt for vision-language models","authors":"Zhou","year":"2022","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhu et al., 2023] Deyao Zhu, Jun Chen, Xiaoqian Shen, Xi- ang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large lan- guage models. arXiv, 2023.","title":"Minigpt-4: Enhancing vision-language understanding with advanced large language models","authors":"Zhu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zou et al., 2022] Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer. Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. page 392-408. Springer-Verlag, 2022.","title":"Spot-the-difference self-supervised pre-training for anomaly detection and segmentation","authors":"Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, Onkar Dabeer","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}