README.md

SVIP: Sequence VerIfication for Procedures in Videos

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

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Getting Started

Prerequisites

• python 3.6
• pytorch 1.7.1
• cuda 10.2

Installation

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

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Datasets

Please refer to here for detailed instructions.

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Training and Evaluation

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.

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Trained Models

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.

Dataset	Split	Papar	Reproduce	ckpt
COIN-SV	val	56.81, 0.4005	58.27, 0.4667	here
	test	51.13, 0.4098	51.55, 0.4658
Diving48-SV	val	91.91, 1.0642	91.69, 1.0928	here
	test	83.11, 0.6009	84.28, 0.6193
CSV	test	83.02, 0.4193	82.88, 0.4474	here

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Citation

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}
}