Language-Assisted Feature Transformation for Anomaly Detection

ICLR 2025 Conference Submission7641 Authors

Keywords: anomaly detection, feature transformation, vision-language model, language guidance

TL;DR: We present Language-Assisted Feature Transformation (LAFT) to guide normality boundaries in image anomaly detection using natural language.

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.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 7641