LECLIP: Boosting Zero-Shot Anomaly Detection With Local Enhanced CLIP

Zero-shot anomaly detection (ZSAD) is a critical task that detects anomalies without any training samples from the target application, which is crucial for applications in diverse fields such as industrial quality control and medical imaging analysis. Recent advances have seen the application of contrastive language-image pretraining (CLIP) in ZSAD, exploiting its robust visual-linguistic alignment and zero-shot learning capabilities. However, CLIP is primarily designed for natural image classification, emphasizing global visual embeddings, while anomaly detection (AD) requires a more accurate representation of anomalous regions and more precise local visual embeddings. To overcome these limitations, this article proposes the local enhanced CLIP (LECLIP) framework for ZSAD. LECLIP incorporates a local alignment (LA) module that divides images into blocks and aligns them with learnable text embeddings, ensuring precise relevance expression. Furthermore, a training-free echo-attention (EA) is proposed to complement the traditional QKV attention, enabling the model to capture both global and local image details effectively, thus providing a more accurate and detailed image representation. Experimental results show that LECLIP achieves superior performance on 15 challenging datasets, including six industrial datasets and nine medical datasets. Code is available at https://github.com/lyy70/LECLIP