MS-RNN: A Flexible Multi-Scale Framework for Spatiotemporal Predictive Learning
PrecipLSTM: A Meteorological Spatiotemporal LSTM for Precipitation Nowcasting
MS-LSTM: Exploring Spatiotemporal Multiscale Representations in Video Prediction Domain
ConvRNNs | MS-RNNs |
---|---|
ConvLSTM | MS-ConvLSTM |
TrajGRU | MS-TrajGRU |
PredRNN | MS-PredRNN |
PredRNN++ | MS-PredRNN++ |
MIM | MS-MIM |
MotionRNN | MS-MotionRNN |
PredRNN-V2 | MS-PredRNN-V2 |
PrecipLSTM | MS-PrecipLSTM |
CMS-LSTM | MS-CMS-LSTM |
MoDeRNN | MS-MoDeRNN |
MK-LSTM | MS-LSTM |
pip3 install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu113
Higher versions of CUDA are not supported, and CUDA 11.1 is recommended. https://blog.csdn.net/qq_40947610/article/details/114757551
cd img_local_att
python setup.py install
https://github.com/zzd1992/Image-Local-Attention
See config.py
python -m torch.distributed.launch --nproc_per_node=4 main.py
@article{ma2022ms,
title={MS-RNN: A flexible multi-scale framework for spatiotemporal predictive learning},
author={Ma, Zhifeng and Zhang, Hao and Liu, Jie},
journal={arXiv preprint arXiv:2206.03010},
year={2022}
}
@article{ma2022preciplstm,
title={PrecipLSTM: A Meteorological Spatiotemporal LSTM for Precipitation Nowcasting},
author={Ma, Zhifeng and Zhang, Hao and Liu, Jie},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={60},
pages={1--8},
year={2022},
publisher={IEEE}
}
@article{ma2023ms,
title={MS-LSTM: Exploring Spatiotemporal Multiscale Representations in Video Prediction Domain},
author={Ma, Zhifeng and Zhang, Hao and Liu, Jie},
journal={Applied Soft Computing},
volume = {147},
pages = {110731},
year={2023},
publisher={Elsevier}
}