MAUCell: An Adaptive Multi-Attention Framework for Video Frame Prediction

• URL: http://arxiv.org/abs/2501.16997v1
• Date: Tue, 28 Jan 2025 14:52:10 GMT
• Title: MAUCell: An Adaptive Multi-Attention Framework for Video Frame Prediction
• Authors: Shreyam Gupta, P. Agrawal, Priyam Gupta,
• Abstract summary: We introduce the Multi-Attention Unit (MAUCell) which combines Generative Adrative Networks (GANs) and attention mechanisms to improve video prediction. The new design system maintains equilibrium between temporal continuity and spatial accuracy to deliver reliable video prediction.
• Score: 0.0
• License:
• Abstract: Temporal sequence modeling stands as the fundamental foundation for video prediction systems and real-time forecasting operations as well as anomaly detection applications. The achievement of accurate predictions through efficient resource consumption remains an ongoing issue in contemporary temporal sequence modeling. We introduce the Multi-Attention Unit (MAUCell) which combines Generative Adversarial Networks (GANs) and spatio-temporal attention mechanisms to improve video frame prediction capabilities. Our approach implements three types of attention models to capture intricate motion sequences. A dynamic combination of these attention outputs allows the model to reach both advanced decision accuracy along with superior quality while remaining computationally efficient. The integration of GAN elements makes generated frames appear more true to life therefore the framework creates output sequences which mimic real-world footage. The new design system maintains equilibrium between temporal continuity and spatial accuracy to deliver reliable video prediction. Through a comprehensive evaluation methodology which merged the perceptual LPIPS measurement together with classic tests MSE, MAE, SSIM and PSNR exhibited enhancing capabilities than contemporary approaches based on direct benchmark tests of Moving MNIST, KTH Action, and CASIA-B (Preprocessed) datasets. Our examination indicates that MAUCell shows promise for operational time requirements. The research findings demonstrate how GANs work best with attention mechanisms to create better applications for predicting video sequences.

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