The field of industrial anomaly detection is moving towards more robust and reliable methods, particularly in complex and unstructured environments. Researchers are focusing on developing innovative approaches that can handle low-quality industrial images, varying views, poses, and illumination conditions. The integration of heterogeneous teacher networks, local-global feature fusion modules, and noise generation modules is showing promising results. Another trend is the use of attention mechanisms and detail enhancement techniques to improve the detection of fine-grained defects. Furthermore, the development of few-shot learning methods is addressing the challenge of limited training data in industrial settings. Notable papers include: FastRef, which proposes a novel prototype refinement framework for few-shot industrial anomaly detection, and YOLO-FDA, which integrates hierarchical attention and detail enhancement for surface defect detection. These advancements are paving the way for more efficient and effective quality control systems in industrial manufacturing.