Spatiotemporal Attention
Spatiotemporal attention mechanisms aim to improve the processing of data with both spatial and temporal dimensions, such as videos and time series, by selectively focusing on relevant information across both space and time. Current research heavily utilizes transformer architectures, often incorporating convolutional neural networks (CNNs) or graph convolutional networks (GCNs) for enhanced feature extraction, and explores various attention modules (e.g., hierarchical, masked, multiscale) to capture complex relationships within the data. This field is significantly impacting diverse applications, including video analysis (summarization, anomaly detection, action recognition), weather forecasting, medical diagnosis (e.g., micro-expression recognition, long COVID outcome prediction), and autonomous driving, by enabling more accurate and efficient processing of dynamic data.