Predrnn: A recurrent neural network for spatiotemporal predictive learning

Y Wang, H Wu, J Zhang, Z Gao, J Wang… - … on Pattern Analysis …, 2022 - ieeexplore.ieee.org
The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms

Y Wang, M Long, J Wang, Z Gao… - Advances in neural …, 2017 - proceedings.neurips.cc
The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical frames, where spatial appearances and temporal variations are …

Simvp: Towards simple yet powerful spatiotemporal predictive learning

C Tan, Z Gao, S Li, SZ Li - arXiv preprint arXiv:2211.12509, 2022 - arxiv.org
Recent years have witnessed remarkable advances in spatiotemporal predictive learning,
incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training …

Openstl: A comprehensive benchmark of spatio-temporal predictive learning

C Tan, S Li, Z Gao, W Guan, Z Wang… - Advances in …, 2023 - proceedings.neurips.cc
Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning

Y Wang, Z Gao, M Long, J Wang… - … on machine learning, 2018 - proceedings.mlr.press
We present PredRNN++, a recurrent network for spatiotemporal predictive learning. In
pursuit of a great modeling capability for short-term video dynamics, we make our network …

Temporal attention unit: Towards efficient spatiotemporal predictive learning

C Tan, Z Gao, L Wu, Y Xu, J Xia… - Proceedings of the …, 2023 - openaccess.thecvf.com
Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics

Y Wang, J Zhang, H Zhu, M Long… - Proceedings of the …, 2019 - openaccess.thecvf.com
Natural spatiotemporal processes can be highly non-stationary in many ways, eg the low-
level non-stationarity such as spatial correlations or temporal dependencies of local pixel …

Convolutional tensor-train LSTM for spatio-temporal learning

J Su, W Byeon, J Kossaifi, F Huang… - Advances in …, 2020 - proceedings.neurips.cc
Learning from spatio-temporal data has numerous applications such as human-behavior
analysis, object tracking, video compression, and physics simulation. However, existing …

Vmrnn: Integrating vision mamba and lstm for efficient and accurate spatiotemporal forecasting

Y Tang, P Dong, Z Tang, X Chu… - Proceedings of the …, 2024 - openaccess.thecvf.com
Abstract Combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs)
with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded …

Shifted chunk transformer for spatio-temporal representational learning

X Zha, W Zhu, L Xun, S Yang… - Advances in Neural …, 2021 - proceedings.neurips.cc
Spatio-temporal representational learning has been widely adopted in various fields such as
action recognition, video object segmentation, and action anticipation. Previous spatio …