Papers › Z-Order Recurrent Neural Networks for Video Prediction

Z-Order Recurrent Neural Networks for Video Prediction

8 Jul 2019IEEE International Conference on Multimedia and Expo (ICME) 2019 7archive 2025-07-28

Jianjin Zhang, Yunbo Wang, Mingsheng Long, Wang Jianmin, Philip S Yu

We present a Z-Order RNN (Znet) for predicting future video frames given historical observations. There are two main contributions respectively in deterministic and stochastic modeling perspective. First, we propose a new RNN architecture for modeling the deterministic dynamics, which updates hidden states along a z-order curve to enhance the consistency of the features of mirrored layers. Second, we introduce an adversarial training approach to two-stream Znet for modeling the stochastic variations, which forces the Znet-Predictor to imitate the behavior of the Znet-Probe. This two-stream architecture enables the adversarial training to be conducted in the feature space instead of the image space. Our model achieves the state-of-the-art prediction accuracy on two video datasets.

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