STONE Deterioration Pattern

Research on "stone" deterioration patterns, while seemingly disparate across various fields, centers on developing efficient and accurate methods for identifying and classifying patterns within complex data. Current efforts leverage deep learning models, including convolutional neural networks (CNNs) and transformer architectures, often employing techniques like supervised contrastive learning, self-supervised learning, and multi-task learning to improve performance and reduce annotation costs. These advancements have implications for diverse applications, ranging from cultural heritage preservation through image analysis to improving the efficiency and accuracy of medical diagnoses and robotic control systems.