Abstract Combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs)
with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded …

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 …

Spatiotemporal predictive learning, which predicts future frames through historical prior
knowledge with the aid of deep learning, is widely used in many fields. Previous work …

Trajectory forecasting is a crucial step for autonomous vehicles and mobile robots in order to
navigate and interact safely. In order to handle the spatial interactions between objects …

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 …

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

Recent advances in deep learning have mainly relied on Transformers due to their data
dependency and ability to learn at scale. The attention module in these architectures …

Spatiotemporal predictive learning offers a self-supervised learning paradigm that enables
models to learn both spatial and temporal patterns by predicting future sequences based on …

Predicting the trajectories of road agents is essential for autonomous driving systems. The
recent mainstream methods follow a static paradigm which predicts the future trajectory by …

Accurate long-term trajectory prediction in complex scenes, where multiple agents (eg,
pedestrians or vehicles) interact with each other and the environment while attempting to …