Foundation Models and Transformers for Anomaly Detection: A Survey
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915404005244928 |
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| author | Ammar, Mouïn Ben Mendoza, Arturo Belkhir, Nacim Manzanera, Antoine Franchi, Gianni |
| author_facet | Ammar, Mouïn Ben Mendoza, Arturo Belkhir, Nacim Manzanera, Antoine Franchi, Gianni |
| contents | In line with the development of deep learning, this survey examines the transformative role of Transformers and foundation models in advancing visual anomaly detection (VAD). We explore how these architectures, with their global receptive fields and adaptability, address challenges such as long-range dependency modeling, contextual modeling and data scarcity. The survey categorizes VAD methods into reconstruction-based, feature-based and zero/few-shot approaches, highlighting the paradigm shift brought about by foundation models. By integrating attention mechanisms and leveraging large-scale pre-training, Transformers and foundation models enable more robust, interpretable, and scalable anomaly detection solutions. This work provides a comprehensive review of state-of-the-art techniques, their strengths, limitations, and emerging trends in leveraging these architectures for VAD. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_15905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundation Models and Transformers for Anomaly Detection: A Survey Ammar, Mouïn Ben Mendoza, Arturo Belkhir, Nacim Manzanera, Antoine Franchi, Gianni Machine Learning Artificial Intelligence In line with the development of deep learning, this survey examines the transformative role of Transformers and foundation models in advancing visual anomaly detection (VAD). We explore how these architectures, with their global receptive fields and adaptability, address challenges such as long-range dependency modeling, contextual modeling and data scarcity. The survey categorizes VAD methods into reconstruction-based, feature-based and zero/few-shot approaches, highlighting the paradigm shift brought about by foundation models. By integrating attention mechanisms and leveraging large-scale pre-training, Transformers and foundation models enable more robust, interpretable, and scalable anomaly detection solutions. This work provides a comprehensive review of state-of-the-art techniques, their strengths, limitations, and emerging trends in leveraging these architectures for VAD. |
| title | Foundation Models and Transformers for Anomaly Detection: A Survey |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15905 |