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.
Abstract (translated)
时间序列建模是视频预测系统、实时预测操作及异常检测应用的基础。在当代时间序列建模中，如何通过高效利用资源实现准确预测仍是一个亟待解决的问题。我们提出了一种结合生成对抗网络（GANs）和时空注意力机制的多注意单元模型（MAUCell），以提升视频帧预测能力。本方法实现了三种类型的注意力模型来捕捉复杂的运动序列。动态组合这些注意力输出使模型能够在保持计算效率的同时，达到高精度决策和高质量结果。引入GAN元素使得生成的帧看起来更逼真，从而使框架能够创建类似于现实世界的输出序列。新的设计系统在时间连续性和空间准确性之间保持平衡，以提供可靠的视频预测。通过结合感知LPIPS测量与经典测试（MSE、MAE、SSIM和PSNR）的全面评估方法表明，在Moving MNIST、KTH Action 和 CASIA-B（预处理）数据集上进行基准测试时，我们的方法比现有方法表现更优。研究表明，MAUCell在运行时间要求上有潜力实现改进。研究结果展示了GANs与注意力机制结合使用以创建更好的视频序列预测应用的优越性。
URL
https://arxiv.org/abs/2501.16997
