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MotionSqueeze: Neural Motion Feature Learning for Video Understanding
<img src="/img/MS_module.png" width="100%" height="100%" alt="MS_module"></img>
This is the implementation of the paper "MotionSqueeze: Neural Motion Feature Learning for Video Understanding" by H.Kwon, M.Kim, S.Kwak, and M.Cho. For more information, checkout the project website and the paper on arXiv.
Environment:
- Cuda: 9.0
- gcc: 7.3.0
- Python 3.6.8
- PyTorch 1.0.1
- TorchVison: 0.2.2
- Spatial Correlation Sampler (https://github.com/ClementPinard/Pytorch-Correlation-extension.git)
- Others: environment
Clone this repo
git clone https://github.com/arunos728/MotionSqueeze.git
Anaconda environment setting
cd MotionSqueeze
conda env create -f environment.yml
conda activate MS
Installing Correlation sampler
cd Pytorch-Correlation-extension
python setup.py install
Please check this repo for the detailed instructions.
Running
- For training TSM or MSNet on Something-v1, use the following command:
./scripts/train_TSM_Something_v1.sh local
- For training TSM or MSNet on Kinetics, use the following command:
./scripts/train_TSM_Kinetics.sh local
- For testing your trained model on Something-v1, use the following command:
./scripts/test_TSM_Something_v1.sh local
- For testing your trained model on Kinetics, use the following command:
./scripts/test_TSM_Kinetics.sh local
Citation
If you use this code or ideas from the paper for your research, please cite our paper:
@inproceedings{kwon2020motionsqueeze,
title={MotionSqueeze: Neural Motion Feature Learning for Video Understanding},
author={Heeseung Kwon and Manjin Kim and Suha Kwak and Minsu Cho},
booktitle={ECCV},
year={2020}
}
Contact
Heeseung Kwon(https://aruno@postech.ac.kr), Manjin Kim(https://mandos@postech.ac.kr)
Questions can also be left as issues in the repository. We will be happy to answer them.