V4D: 4D Convolutional Neural Networks for Video-level Representation Learning

Code for the ICLR 2020 paper V4D: 4D Convolutional Neural Networks for Video-level Representation Learning

Performance compared with SOTA methods on Kinetics

Model	Backbone	Top1	Top5
ARTNet with TSN	ARTNet ResNet18	70.7	89.3
ECO	BN-Inception+3D ResNet18	70.0	89.4
S3D-G	S3D Inception	74.7	93.4
Nonlocal Network	3D ResNet50	76.5	92.6
SlowFast	SlowFast ResNet50	77.0	92.6
I3D	I3D Inception	72.1	90.3
Two-stream I3D	I3D Inception	75.7	92.0
I3D-S	Slow pathway ResNet50	74.9	91.5
Ours V4D	V4D ResNet50	77.4	93.1

Requirements

• Python >=3.6
• PyTorch >=1.3
• torchvision that matches the PyTorch installation.

Train on Kinetics and Mini-kinetics

./scripts/train_kinetics.sh
./scripts/train_minikinetics.sh

Test pretrained model on Mini-Kinetics-200 and Kinetics (download our trained model from Google)

./scripts/test_kinetics.sh
./scripts/test_minikinetics.sh

Contact

For any questions, please feel free to reach

github@malongtech.com

If you use this method or this code in your research, please cite as:

@inproceedings{zhang2020v4d,
title={V4D: 4D Convolutional Neural Networks for Video-level Representation Learning},
author={Zhang, Shiwen and Guo, Sheng and Huang, Weilin and Scott, Matthew R and Wang, Limin},
booktitle={Proceedings of International Conference on Learning Representations},
year={2020}
}

License

V4D is CC-BY-NC 4.0 licensed, as found in the LICENSE file. It is released for academic research / non-commercial use only. If you wish to use for commercial purposes, please contact sales@malongtech.com.