Hierarchical Spatial Temporal Transformer

Hierarchical Spatial-Temporal Transformers (HSTTs) are a class of deep learning models designed to effectively capture complex spatiotemporal dependencies in data, particularly in time series with hierarchical structures. Current research focuses on applying HSTTs to diverse problems, including long-term forecasting (e.g., wind power), cross-subject analysis (e.g., EEG-based emotion recognition), and human pose estimation, often employing hourglass-shaped encoder-decoder architectures with parallel transformer skeletons and multi-level fusion mechanisms. These models offer improved accuracy and generalization compared to previous methods by explicitly addressing the varying scales and complexities inherent in spatiotemporal data, leading to advancements in various fields requiring precise analysis of dynamic systems.