Temporal Transformer

Temporal transformers are deep learning models designed to analyze and predict sequences of data with inherent spatio-temporal dependencies, aiming to improve upon traditional methods by capturing long-range interactions across both space and time. Current research focuses on applying these models to diverse applications, including traffic prediction, weather forecasting, video analysis (deblurring, object segmentation, action recognition), and human motion analysis, often employing architectures that combine transformers with convolutional neural networks or graph neural networks to leverage different types of data representations. The resulting advancements have significant implications for various fields, offering improved accuracy and efficiency in tasks ranging from autonomous driving to medical diagnosis.