Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

Recent years have witnessed remarkable advances in spatiotemporal predictive learning,
incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training …

The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

Spatiotemporal prediction is challenging due to the complex dynamic motion and
appearance changes. Existing work concentrates on embedding additional cells into the …

We present PredRNN++, a recurrent network for spatiotemporal predictive learning. In
pursuit of a great modeling capability for short-term video dynamics, we make our network …

The widespread deployment of wireless and mobile devices results in a proliferation of
spatio-temporal data that is used in applications, eg, traffic prediction, human mobility …

Abstract Integrating CNNs and RNNs to capture spatiotemporal dependencies is a prevalent
strategy for spatiotemporal prediction tasks. However, the property of CNNs to learn local …

Effective long-term predictions have been increasingly demanded in urban-wise data mining
systems. Many practical applications, such as accident prevention and resource pre …

Spatio-temporal prediction is a key type of tasks in urban computing, eg, traffic flow and air
quality. Adequate data is usually a prerequisite, especially when deep learning is adopted …