Abstract
Video prediction is a complex time-series forecasting task with great potential in many use cases. However, conventional methods overemphasize accuracy while ignoring the slow prediction speed caused by complicated model structures that learn too much redundant information with excessive GPU memory consumption. Furthermore, conventional methods mostly predict frames sequentially (frame-by-frame) and thus are hard to accelerate. Consequently, valuable use cases such as real-time danger prediction and warning cannot achieve fast enough inference speed to be applicable in reality. Therefore, we propose a transformer-based keypoint prediction neural network (TKN), an unsupervised learning method that boost the prediction process via constrained information extraction and parallel prediction scheme. TKN is the first real-time video prediction solution to our best knowledge, while significantly reducing computation costs and maintaining other performance. Extensive experiments on KTH and Human3.6 datasets demonstrate that TKN predicts 11 times faster than existing methods while reducing memory consumption by 17.4% and achieving state-of-the-art prediction performance on average.
Abstract (translated)
视频预测是一种在许多使用场景中具有巨大潜力的复杂的时间序列预测任务。然而,传统的方法过于强调准确性,而忽视了由于复杂的模型结构导致学习过多的冗余信息以及GPU内存消耗过高所带来的缓慢预测速度。此外,传统的方法大多顺序预测帧(帧一帧),因此很难加速。因此,我们提出了基于Transformer的关键帧预测神经网络(TKN),一种无监督学习方法,通过限制信息提取和并行预测方案来增强预测过程。TKN是我们所知的实时视频预测解决方案的第一个方法,同时显著降低计算成本并维持其他性能。在KTH和Human3.6数据集上进行广泛的实验表明,TKN预测速度比现有方法快11倍,同时减少了内存消耗,平均实现最先进的预测性能。
URL
https://arxiv.org/abs/2303.09807