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. Self-Regulated Learning for Egocentric Video Activity Anticipation. TPAMI 2021.

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

For the raw data of the EPIC-Kitchens dataset, please refer to https://github.com/epic-kitchens/download-scripts to download.

For the three modality features (rgb, flow, obj), please refer to https://github.com/fpv-iplab/rulstm to download. After downloading, put them in the folder './data'.

For the raw data of the EGTEA Gaze+ dataset, please refer to http://cbs.ic.gatech.edu/fpv/ to download.

For the extracted features, please refer to https://github.com/fpv-iplab/rulstm to download. After downloading, put them in the folder './data'.

For the raw data of the 50 Salads dataset, please refer to http://cvip.computing.dundee.ac.uk/datasets/foodpreparation/50salads/ to download.

For the extracted features, please refer to https://github.com/colincsl/TemporalConvolutionalNetworks to download. After downloading, put them in the folder './data'.

For the raw data of the Breakfast dataset, please refer to https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/ to download.

For the extraced I3D features, please download from Baidu passward: 'wub3' or Google Drive. After downloading, put them in the folder './data'.

For rgb feature, python main.py --gpu_ids 0 --batch_size 128 --wd 1e-5 --lr 0.1 --reinforce_verb_weight 0.01 --reinforce_noun_weight 0.01 --revision_weight 0.8 --mode train --modality rgb --hidden 1024 --feat_in 1024

Silimar commonds can be used for flow or obj features.

Please download the pre-trained model weigths from Baidu passward: 'wub3' or Google Drive, and put them in the folder './results/EPIC/base_srl/pre_trained/'.

For rgb feature, python main.py --gpu_ids 0 --batch_size 128 --mode validate --modality rgb --hidden 1024 --feat_in 1024 --resume_timestamp pre_trained

For flow feature, python main.py --gpu_ids 0 --batch_size 128 --mode validate --modality flow --hidden 1024 --feat_in 1024 --resume_timestamp pre_trained

For obj feature, python main.py --gpu_ids 0 --batch_size 128 --mode validate --modality obj --hidden 352 --feat_in 352 --resume_timestamp pre_trained

For three modality features, python main.py --gpu_ids 0 --batch_size 128 --mode validate --modality fusion --resume_timestamp pre_trained

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

@article{qi2021self,
  title={Self-Regulated Learning for Egocentric Video Activity Anticipation},
  author={Qi, Zhaobo and Wang, Shuhui and Su, Chi and Su, Li and Huang, Qingming and Tian, Qi},
  journal={IEEE Transactions on Pattern Analysis \& Machine Intelligence},
  number={01},
  pages={1--1},
  year={2021},
  publisher={IEEE Computer Society}
}

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

• zhaobo.qi@vipl.ict.ac.cn