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

• zhaobo.qi@vipl.ict.ac.cn