Language-Assisted Feature Transformation for Anomaly Detection
Journal:
arXiv
Published Date:
Mar 3, 2025
Abstract
This paper introduces LAFT, a novel feature transformation method designed to
incorporate user knowledge and preferences into anomaly detection using natural
language. Accurately modeling the boundary of normality is crucial for
distinguishing abnormal data, but this is often challenging due to limited data
or the presence of nuisance attributes. While unsupervised methods that rely
solely on data without user guidance are common, they may fail to detect
anomalies of specific interest. To address this limitation, we propose
Language-Assisted Feature Transformation (LAFT), which leverages the shared
image-text embedding space of vision-language models to transform visual
features according to user-defined requirements. Combined with anomaly
detection methods, LAFT effectively aligns visual features with user
preferences, allowing anomalies of interest to be detected. Extensive
experiments on both toy and real-world datasets validate the effectiveness of
our method.
