Core Viewpoint - The article discusses the SeaS model, a unified few-shot industrial anomaly generation method that addresses the challenges of generating diverse anomaly samples and precise mask annotations in industrial quality inspection, significantly improving the performance of downstream anomaly detection tasks [3][45]. Group 1: Model Overview - SeaS utilizes a unified framework that requires only 1-3 training samples to simultaneously achieve diverse anomaly generation, consistent normal product synthesis, and pixel-level precise mask annotation, setting a new benchmark in the field [9][45]. - The model leverages a separation and sharing fine-tuning mechanism to model the different change patterns of normal products and anomalies, enhancing the precision of the generation process while maintaining the diversity of anomalies and consistency of normal products [10][45]. Group 2: Technical Innovations - SeaS introduces three major innovations: a unified few-shot generation framework, a separation and sharing fine-tuning mechanism, and a refined mask prediction branch that integrates U-Net discriminative features with high-resolution VAE features for pixel-level accurate anomaly labeling [8][10][45]. - The model employs an unbalanced anomaly text prompt structure to effectively represent the inherent differences between normal and abnormal products, ensuring precise control over the changes in anomaly regions [15][45]. Group 3: Performance Metrics - SeaS outperforms existing few-shot industrial anomaly generation methods across key metrics on mainstream industrial datasets such as MVTec AD and VisA, with an average improvement of 12.79% in anomaly segmentation IoU [7][32][41]. - The generated data from SeaS significantly enhances the performance of supervised segmentation models, with notable improvements in metrics such as AUROC and pixel-level accuracy across various datasets [38][41][43]. Group 4: Practical Applications - The generated anomaly samples from SeaS can be effectively applied to synthetic data-based detection methods, leading to significant improvements in detection performance and a reduction in false negatives across multiple datasets [37][45]. - The model's ability to generate high-quality normal images also aids in augmenting training datasets for unsupervised detection methods, resulting in reduced false positives and optimized performance metrics [37][41].