Global Feature

Global feature extraction aims to capture comprehensive, high-level representations of data, enabling improved performance in various tasks such as image classification, object recognition, and time series generation. Current research emphasizes hybrid architectures combining convolutional neural networks (CNNs) for local feature extraction with transformers for global context modeling, often incorporating attention mechanisms to enhance feature selectivity and efficiency. These advancements are driving improvements in accuracy and speed across diverse applications, from medical image analysis and remote sensing to acoustic signal processing and social network analysis. The ability to effectively leverage global features is crucial for tackling complex data heterogeneity and improving the robustness and scalability of machine learning models.