Spatio Temporal Representation

Spatio-temporal representation focuses on modeling data that changes over both space and time, aiming to capture complex interactions and dependencies within dynamic systems. Current research heavily utilizes deep learning architectures, including transformers, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), often combined for enhanced performance in tasks like video analysis, trajectory prediction, and urban change detection. These advancements have significant implications for various fields, improving accuracy and efficiency in applications ranging from autonomous driving and healthcare to environmental monitoring and human-computer interaction. The development of robust and efficient spatio-temporal representations remains a key challenge and active area of investigation.