Taylor saves for later: disentanglement for video prediction using Taylor representation
This addresses the challenge of balancing short-term and long-term prediction accuracy in video prediction, which has applications in meteorology and robotics.
The paper tackles the video prediction problem by proposing a two-branch sequence-to-sequence model that disentangles Taylor features and residual features in video frames, achieving state-of-the-art or competitive performance on three datasets (Moving MNIST, TaxiBJ, Human 3.6).
Video prediction is a challenging task with wide application prospects in meteorology and robot systems. Existing works fail to trade off short-term and long-term prediction performances and extract robust latent dynamics laws in video frames. We propose a two-branch seq-to-seq deep model to disentangle the Taylor feature and the residual feature in video frames by a novel recurrent prediction module (TaylorCell) and residual module. TaylorCell can expand the video frames' high-dimensional features into the finite Taylor series to describe the latent laws. In TaylorCell, we propose the Taylor prediction unit (TPU) and the memory correction unit (MCU). TPU employs the first input frame's derivative information to predict the future frames, avoiding error accumulation. MCU distills all past frames' information to correct the predicted Taylor feature from TPU. Correspondingly, the residual module extracts the residual feature complementary to the Taylor feature. On three generalist datasets (Moving MNIST, TaxiBJ, Human 3.6), our model outperforms or reaches state-of-the-art models, and ablation experiments demonstrate the effectiveness of our model in long-term prediction.