DPRF-CLIP:Dual-Path Residual Fusion for Zero-Shot Industrial Anomaly Detection
Abstract
Zero-Shot Anomaly Detection (ZSAD) aims to accurately identify anomalous samples from unseen categories without relying on target class training data. In industrial quality inspection scenarios, collecting training samples for target defect categories is often impractical due to production constraints and data scarcity, and ZSAD methods can effectively address the challenge of reliable anomaly detection under limited data conditions. Recently, vision-language models have shown strong generalization and inherent zero-shot capabilities, greatly facilitating their wide application in zero-shot industrial anomaly detection tasks with competitive and reliable detection performance. However, they have critical practical limitations: insufficient attention to fine-grained image details and poor adaptability to the specific requirements of industrial anomaly detection tasks. To address these limitations, we propose DPRF-CLIP, a CLIP-based ZSAD framework. It uses a pre-trained ResNet network to extract fine-grained local image features, which are fused into CLIP’s visual encoder via a specially designed bidirectional cross-attention module. A feature enhancement module is also integrated to further strengthen the model’s ability in capturing fine-grained local visual patterns. Comprehensive experiments on real-world industrial benchmarks (MVTec AD, VisA) show DPRF-CLIP achieves competitive performances, validating its effectiveness and strong generalization in industrial anomaly detection.