Foundation Models and Transformers for Anomaly Detection: A Survey
It provides a comprehensive review for researchers and practitioners in computer vision, but is incremental as it synthesizes existing work rather than introducing new methods.
This survey examines how Transformers and foundation models address challenges in visual anomaly detection, such as long-range dependencies and data scarcity, by categorizing methods and highlighting their impact on robustness and scalability.
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.