DE-CLIP: Few-Shot Anomaly Detection via Difference-Guided Embedding Editing

Anomaly detection (AD) plays a critical role in applications such as automated industrial inspection and medical image analysis. Empowered by the strong pre-trained vision-language model, CLIP, recent years have witnessed the emergence of several CLIP-based few-shot AD methods. Due to the overlap between the embedding distributions of normal and anomalous samples, many existing approaches introduce additional model training for more discriminative text embeddings. However, we demonstrate that such training is not necessary. Specifically, we find that this embedding overlap can be separated by introducing a Differenceguided vector for embedding Editing (DiffEdit). Based on this finding, we propose DE-CLIP, a simple yet effective framework based on DiffEdit, which directly edits text embeddings based on the textual and visual differences between normal and anomalous samples, resulting in more discriminative embeddings for AD. Extensive experiments on industrial and medical datasets demonstrate the superiority of our proposed DE-CLIP compared with existing baselines. For instance, on the MVTec dataset, DE-CLIP achieves 96.6% and 96.7% AUROC on anomaly classification and segmentation, surpassing both training-based and training-free methods. In addition, we observe that introducing DiffEdit into other trainingfree baselines could also significantly improve their performance, highlighting the potential of DiffEdit to promote better AD. 1