Global Encoder

Global encoders are a key component in many recent deep learning models, aiming to capture high-level, context-rich representations from diverse data types, including images, point clouds, and time series. Current research focuses on integrating global encoders with other architectures, such as transformers and convolutional neural networks, to improve feature extraction and fusion, often incorporating mechanisms to balance global context with local details. This approach enhances performance in various applications, including image classification, video captioning, and personalized federated learning, by providing more comprehensive and informative representations for downstream tasks.