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Grouped Spatial-Temporal Aggretation for Efficient Action Recognition

Pytorch implementation of paper Grouped Spatial-Temporal Aggretation for Efficient Action Recognition. arxiv

Prerequisites

  • PyTorch 1.0 or higher
  • python 3.5 or higher

Data preparation

Please refer to TRN-pytorch for data preparation on Something-Something.

Training

  • For GST-Large: python3 main.py --root_path /path/to/video/folder --dataset somethingv1 --checkpoint_dir /path/for/saving/checkpoints/ --type GST --arch resnet50 --num_segments 8 --beta 1
  • For GST: python3 main.py --root_path /path/to/video/folder --dataset somethingv1 --checkpoint_dir /path/for/saving/checkpoints/ --type GST --arch resnet50 --num_segments 8 --beta 2 --alpha 4
  • For more details, please type python3 main.py -h

Pretrained Models

Something-v1 Something-v2
GST(alpha=4, 8 frames) 47.0 61.6
GST(alpha=4,16 frames) 48.6 62.6
GST-Large(alpha=4,8 frames) 47.7 62.0
  • results are reported based on center crop and 1 clip sampling.

Reference

If you find our work useful in your research, please consider citing our paper

@inproceedings{luo2019grouped,
  title={Grouped Spatial-Temporal Aggretation for Efficient Action Recognition},
  author={Luo, Chenxu and Yuille, Alan},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2019}
}

or

@article{luo2019grouped,
  title={Grouped Spatial-Temporal Aggregation for Efficient Action Recognition},
  author={Luo, Chenxu and Yuille, Alan},
  journal={arXiv preprint arXiv:1909.13130},
  year={2019}
}

Acknowledge

This codebase is build upon TRN-pytorch and TSN-pytorch