Vision Augmentation Prediction Autoencoder with Attention Design (VAPAAD)

arXiv:2404.10096v2 Announce Type: replace
Abstract: Recent advancements in sequence prediction have significantly improved the accuracy of video data interpretation; however, existing models often overlook the potential of attention-based mechanisms for next-frame prediction. This study introduces the Vision Augmentation Prediction Autoencoder with Attention Design (VAPAAD), an innovative approach that integrates attention mechanisms into sequence prediction, enabling nuanced analysis and understanding of temporal dynamics in video sequences. Utilizing the Moving MNIST dataset, we demonstrate VAPAAD's robust performance and superior handling of complex …

abstract accuracy arxiv attention attention mechanisms augmentation autoencoder cs.ai cs.cv data design however interpretation next prediction study type video video data vision