GSSTU: Generative Spatial Self-Attention Transformer Unit for Enhanced Video Prediction.

Future frame prediction is a challenging task in computer vision with practical applications in areas such as video generation, autonomous driving, and robotics. Traditional recurrent neural networks have limited effectiveness in capturing long-range dependencies between frames, and combining convolutional neural networks (CNNs) with recurrent networks has limitations in modeling complex dependencies. Generative adversarial networks have shown promising results, but they are computationally expensive and suffer from instability during training.