  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
temporal data compared to traditional methods. VAPAAD combines data
augmentation, ConvLSTM2D layers, and a custom-built self-attention mechanism to
effectively focus on salient features within a sequence, enhancing predictive
accuracy and context-aware analysis. This methodology not only adheres to human
cognitive processes during video interpretation but also addresses limitations
in conventional models, which often struggle with the variability inherent in
video sequences. The experimental results confirm that VAPAAD outperforms
existing models, especially in integrating attention mechanisms, which
significantly improve predictive performance.
