Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection

1. 1Utilize bidirectional multimodal prompt learning to leverage both vision and language modalities
2. 2Implement scale-aware training to adapt to varying defect patterns across categories
3. 3Apply few-shot learning techniques to accommodate scarce normal samples
4. 4Evaluate the approach on real-world industrial datasets to assess its effectiveness

AI engineers and researchers benefit from this approach as it improves the accuracy and efficiency of anomaly detection in industrial settings, while data scientists can apply these methods to real-world problems

💡 Bidirectional multimodal prompt learning with scale-aware training improves the accuracy and efficiency of few-shot multi-class anomaly detection

🚀 Enhance few-shot multi-class anomaly detection with bidirectional multimodal prompt learning!