Colar: Effective and Efficient Online Action Detection by Consulting Exemplars

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.

Illustrating the architecture of the proposed I2Sim

Requirements

To install requirements:

conda env create -n env_name -f environment.yaml

Before running the code, please activate this conda environment.

Data Preparation

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

Train

a. Config

Adjust configurations according to your machine.

./misc/init.py

c. Train

python main.py

Inference

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

Citation

@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}
}

Related Projects

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

Contact

For any discussions, please contact [email protected].

GitHub

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