Yulim So

AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfer

Anomaly generation has been widely explored to address the scarcity of anomaly images in real-world data. However, existing methods typically suffer from at least one of the following limitations, hindering their practical deployment: (1) lack of visual realism in generated anomalies; (2) dependence