@inproceedings{93c007a542b54d3da6a20ca4782c717e,
title = "SARNN: A Spatiotemporal Prediction Model for Reducing Error Transmissions",
abstract = "Spatiotemporal prediction has become an important research topic in weather forecasting and traffic planning. Due to the cyclic structure for prediction images frame by frame, the error generation and accumulation has often led to blurred images. In this paper, we propose a new end-to-end spatiotemporal attention recurrent neural network (SARNN) to overcome this problem. A new cyclic core mechanism based on long-short term memory (LSTM) is used for extracting the directions of spatial correlation and temporal evolution feature separately. Specifically, an attention mechanism added in temporal direction allows for adaptively choosing highlight input time step of hidden state, instead of decoder just relying on the output of previous time step; a scale change convolution block has been added in the spatial direction to enhance the capability of extraction multi-level semantic features. The validation experiment on Moving-Mnist and KTH dataset demonstrates that SARNN can output more accurate and clearer prediction frames.",
keywords = "Attention mechanism, LSTM, RNN based model, Spatiotemporal prediction",
author = "Yonghui Liang and Lu Zhang and Yuqing He and Na Xu and Mingqi Liu and Mahr, {Jeremy Jianshuo li}",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 6th Asian Conference on Pattern Recognition, ACPR 2021 ; Conference date: 09-11-2021 Through 12-11-2021",
year = "2022",
doi = "10.1007/978-3-031-02375-0_10",
language = "English",
isbn = "9783031023743",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "130--143",
editor = "Christian Wallraven and Qingshan Liu and Hajime Nagahara",
booktitle = "Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers",
address = "Germany",
}