TF²: Few-Shot Text-Free Training-Free Defect Image Generation for Industrial Anomaly Inspection

Qianzi Yu, Kai Zhu, Yang Cao, Feijie Xia, Yu Kang

Abstract: Anomaly inspection aims at identifying various defects in real time on modern industrial production lines. However, due to insufficient anomaly data, existing detectors cannot effectively accomplish the classification of defects, thereby failing to provide guidance for subsequent production. To address it, we propose TF2, a few-shot text-free training-free defect image generation method, which jointly models the image distribution of class-agnostic defects and backgrounds, achieving efficient semantic enhancement. Firstly, we propose the Response Alignment Strategy, which merges the reversed latent space of both defect-free and defective samples, generating new defect images not limited to textual descriptions yet with consistent content. Moreover, we introduce the Defect Moving Strategy and the Regional Average Loss to merge the reversed latent space of the moving areas and enhance the variability of detail features, increasing both the location and content diversity of defects. Extensive experiments demonstrate the superiority of our model over the state-of-the-art competitors. The metrics indicate that our generated anomaly data focuses on balancing both image quality and diversity, effectively improving the performance of downstream anomaly inspection tasks.

External IDs:dblp:journals/tcsv/YuZCXK24