Spatiotemporal Convolutional Neural Network

Spatiotemporal convolutional neural networks (ST-CNNs) analyze data with both spatial and temporal dimensions, aiming to model dynamic processes in various domains. Current research focuses on improving ST-CNN architectures, such as integrating them with transformers or employing techniques like depth-wise separable convolutions and hierarchical residual learning to enhance efficiency and performance. These advancements are proving valuable in diverse applications, including medical image analysis (e.g., perfusion mapping), video understanding (e.g., action recognition, video prediction), and multimodal data fusion (e.g., valence-arousal estimation), leading to more accurate and efficient analysis of complex dynamic systems.