Awesome
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.
<image src="imgs/task.png" width="700">Getting Started
Prerequisites
- python 3.6
- pytorch 1.7.1
- cuda 10.2
Installation
-
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
-
Download the Kinetics-400 pretrained model.
Link:here
Extraction code:bs6b
Datasets
Please refer to here for detailed instructions.
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:
Note that we use L2 distance while evaluating on COIN-SV, otherwise NormL2.CUDA_VISIBLE_DEVICES=0 python eval.py --config configs/eval_resnet_config.yml --root_path [model&log folder] --dist [L2/NormL2] --log_name [xxx]
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.
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}
}