/CKMN

Primary LanguagePython

Towards More Explainability: Concept Knowledge Mining Network for Event Recognition

This is a PyTorch implementation of the model described in our paper:

Z. Qi, S. Wang, C. Su, L. Su, Q. Huang, and Q. Tian. Towards More Explainability: Concept Knowledge Mining Network for Event Recognition. ACM MM 2020.

Dependencies

  • Pytorch 1.0.1
  • Cuda 9.0.176
  • Cudnn 7.4.2
  • Python 3.6.8

Data

Dataset Prepare

  1. Download the pre-trained concept detector weights from Baidu passward 'wv0e' or Google Grive and put them in folder weights/

  2. Download the FCVID dataset from http://bigvid.fudan.edu.cn/FCVID/.

  3. The annotation information of each dataset is provided in folder data/FCVID/video_labels.

  4. Extract the video frames for each video and put the extracted frames in folder data/FCVID/frames/.

    For ActivityNet dataset ( http://activity-net.org/.) , we use the latest released version of the dataset (v1.3).

Train

  • python main.py --gpu_ids 0,1 --dataset FCVID --no_test

    for other hyperparameters, please refer to opts.py file.

Test

  • Pretrained model weigths are avaiable in Baidu passward '7uq8' or Google Grive

  • Download the pre-trained weights and put them in folder results/

  • python main.py --gpu_ids 0,1 --dataset FCVID --resume_path pretrained_model/CKMN.pth --no_train --test_crop_number 1

Citation

Please cite our paper if you use this code in your own work:

@inproceedings{qi2020towards,
  title={Towards More Explainability: Concept Knowledge Mining Network for Event Recognition},
  author={Qi, Zhaobo and Wang, Shuhui and Su, Chi and Su, Li and Huang, Qingming and Tian, Qi},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={3857--3865},
  year={2020}
}

Contcat

If you have any problem about our code, feel free to contact