Spatiotemporal Learning

Spatiotemporal learning focuses on extracting meaningful patterns from data exhibiting both spatial and temporal dependencies, aiming to improve prediction accuracy and understanding of dynamic systems. Current research emphasizes developing efficient and robust model architectures, including graph neural networks, transformers (like UniFormerV2), and masked autoencoders (like AdaMAE), often incorporating techniques like meta-learning and adaptive masking to handle heterogeneity and improve generalization. These advancements are driving progress in diverse fields such as video analysis, urban planning (smart cities), and industrial process monitoring (e.g., additive manufacturing), where accurate spatiotemporal predictions are crucial for optimization and decision-making. The development of new benchmarks and datasets is also fostering more rigorous and comprehensive evaluation of these models.