Pytorch Implementation of 'Background Suppression Network for Weakly-supervised Temporal Action Localization' (AAAI 2020)
- Python 3.5
- Pytorch 1.0
- Tensorflow 1.15 (for Tensorboard)
You can set up the environments by using $ pip3 install -r requirements.txt
.
-
Prepare THUMOS'14 dataset.
- We excluded three test videos (270, 1292, 1496) as previous work did.
-
Extract features with two-stream I3D networks
-
Place the features inside the
dataset
folder.- Please ensure the data structure is as below.
├── dataset
└── THUMOS14
├── gt.json
├── split_train.txt
├── split_test.txt
└── features
├── train
├── rgb
├── video_validation_0000051.npy
├── video_validation_0000052.npy
└── ...
└── flow
├── video_validation_0000051.npy
├── video_validation_0000052.npy
└── ...
└── test
├── rgb
├── video_test_0000004.npy
├── video_test_0000006.npy
└── ...
└── flow
├── video_test_0000004.npy
├── video_test_0000006.npy
└── ...
You can easily train and evaluate BaS-Net by running the script below.
If you want to try other training options, please refer to options.py
.
$ bash run.sh
The pre-trained model can be found here. You can evaluate the model by running the command below.
$ bash run_eval.sh
We referenced the repos below for the code.
If you find this code useful, please cite our paper.
@inproceedings{lee2020background,
title={Background Suppression Network for Weakly-supervised Temporal Action Localization},
author={Lee, Pilhyeon and Uh, Youngjung and Byun, Hyeran},
booktitle={AAAI},
year={2020}
}
If you have any question or comment, please contact the first author of the paper - Pilhyeon Lee (lph1114@yonsei.ac.kr).