Vision Augmentation Prediction Autoencoder with Attention Design (VAPAAD)

Despite significant advancements in sequence prediction, current methods lack attention-based mechanisms for next-frame prediction. Our work introduces VAPAAD or Vision Augmentation Prediction Autoencoder with Attention Design, an innovative model that enhances predictive performance by integrating attention designs, allowing for nuanced understanding and handling of temporal dynamics in video sequences. We demonstrate using the famous Moving MNIST dataset the robust performance of the proposed model and potential applicability of such design in the literature.