3 accepted papers
Generalist Anomaly Detection (GAD) seeks to overcome the domain-specific limitations of traditional anomaly detection by training a unified model that can generalize to unseen classes. A promising GAD strategy involves using residual features to create a class-invariant space. However, existing meth
Vision-language models bridge visual and linguistic understanding and have proven to be powerful for video recognition tasks. Existing methods primarily rely on parameter-efficient fine-tuning of pre-trained image-text models, suffering from limited interpretability and poor generalization due to in
Generalist Anomaly Detection (GAD) aims to train a unified model on an original domain that can detect anomalies in new target domains. Previous GAD methods primarily use only normal samples as references, overlooking the valuable information contained in anomalous samples that are often available i…