MotionSqueeze: Neural Motion Feature Learning for Video Understanding

---

---

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: 5.4.0
• Python 3.6.8
• PyTorch 1.0.1
• TorchVison: 0.2.1
• Spatial Correlation Sampler (https://github.com/ClementPinard/Pytorch-Correlation-extension.git)
• Others: conda_list

Clone this repo

git clone https://github.com/arunos728/MotionSqueeze.git

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://[email protected]), Manjin Kim(https://[email protected])

Questions can also be left as issues in the repository. We will be happy to answer them.