3D Temporal Convolutional Transformer Network

3D Temporal Convolutional Transformer Networks (3D-TCNs) process spatiotemporal data, aiming to improve predictions and analyses across various domains by leveraging both convolutional and transformer architectures. Current research focuses on applying 3D-TCNs to diverse applications, including gesture recognition from radar data, compressing and analyzing 4D visual data, and improving remote sensing image reconstruction and precipitation nowcasting. These advancements demonstrate the power of 3D-TCNs in handling complex, high-dimensional data, leading to improved accuracy and efficiency in diverse scientific and practical applications.