This repo is the official implementation of our CVPR 2022 paper: SVIP: Sequence VerIfication for Procedures in Videos.

• python 3.6
• pytorch 1.7.1
• cuda 10.2

1. Clone the repo and install dependencies.

   git clone https://github.com/svip-lab/SVIP-Sequence-VerIfication-for-Procedures-in-Videos.git
   cd VIP-Sequence-VerIfication-for-Procedures-in-Videos
   pip install requirements.txt 

2. Download the Kinetics-400 pretrained model.

   Link：here

   Extraction code：bs6b

Please refer to here for detailed instructions.

We have provided the default configuration files for reproducing our results. Try these commands to play with this project.

• For training:

  CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --config configs/train_resnet_config.yml

• For evaluation:

  CUDA_VISIBLE_DEVICES=0 python eval.py --config configs/eval_resnet_config.yml --root_path [model&log folder] --dist [L2/NormL2] --log_name [xxx]

  Note that we use L2 distance while evaluating on COIN-SV, otherwise NormL2.

We provide checkpoints for each dataset trained with this re-organized codebase.

Notice: The reproduced performances are occassionally higher or lower (within a reasonable range) than the results reported in the paper.

If you find this repo helpful, please cite our paper:

@inproceedings{qian2022svip,
  title={SVIP: Sequence VerIfication for Procedures in Videos},
  author={Qian, Yicheng and Luo, Weixin and Lian, Dongze and Tang, Xu and Zhao, Peilin and Gao, Shenghua},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19890--19902},
  year={2022}
}