This repository is the official implementation of Colar. In this work, we study the online action detection and develop an effective and efficient exemplar-consultation mechanism. Paper from arXiv.

To install requirements:

conda env create -n env_name -f environment.yaml

Before running the code, please activate this conda environment.

a. Download pre-extracted features from baiduyun (code:cola)

Please ensure the data structure is as below

├── data
   └── thumos14
       ├── Exemplar_Kinetics
       ├── thumos_all_feature_test_Kinetics.pickle
       ├── thumos_all_feature_val_Kinetics.pickle
       ├── thumos_test_anno.pickle
       ├── thumos_val_anno.pickle
       ├── data_info.json

a. Config

Adjust configurations according to your machine.

./misc/init.py

c. Train

python main.py

a. You can download pre-trained models from baiduyun (code:cola), and put the weight file in the folder checkpoint.

• The performance of our model is 66.9% mAP.

b. Test

python inference.py

@inproceedings{yang2022colar,
  title={Colar: Effective and Efficient Online Action Detection by Consulting Exemplars},
  author={Yang, Le and Han, Junwei and Zhang, Dingwen},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

• BackTAL: Background-Click Supervision for Temporal Action Localization.

For any discussions, please contact nwpuyangle@gmail.com.