VividLe / BackTAL

The official implementation of BackTAL, TPAMI 2021.

Background-Click Supervision for Temporal Action Localization

This repository is the official implementation of BackTAL. In this work, we convert existing action-click supervision to the background-click supervision and develop a novel method, called BackTAL. Paper from arXiv or IEEE.

Requirements

To install requirements:

conda env create -f environment.yaml

Data Preparation

Download

Download pre-extracted I3D features of Thumos14, ActivityNet1.2 and HACS dataset from BaiduYun with code back.

Please ensure the data structure is as below

├── data
   └── Thumos14
       ├── val
           ├── video_validation_0000051.npz
           ├── video_validation_0000052.npz
           └── ...
       └── test
           ├── video_test_0000004.npz
           ├── video_test_0000006.npz
           └── ...
   └── ActivityNet1.2
       ├── training
           ├── v___dXUJsj3yo.npz
           ├── v___wPHayoMgw.npz
           └── ...
       └── validation
           ├── v__3I4nm2zF5Y.npz
           ├── v__8KsVaJLOYI.npz
           └── ...
   └── HACS
       ├── training
           ├── v_0095rqic1n8.npz
           ├── v_62VWugDz1MY.npz
           └── ...
       └── validation
           ├── v_008gY2B8Pf4.npz
           ├── v_00BcXeG1gC0.npz
           └── ...
     

Background-Click Annotations

The raw annotations of THUMOS14 dataset are under directory './data/THUMOS14/human_anns'.

Evaluation

Pre-trained Models

You can download checkpoints for Thumos14, ActivityNet1.2 and HACS dataset from BaiduYun with code back. These models are trained on Thumos14, ActivityNet1.2 or HACS using the configuration file under the directory "./experiments/". Please put these checkpoints under directory "./checkpoints".

Evaluation

Before running the code, please activate the conda environment.

To evaluate BackTAL model on Thumos14, run:

cd ./tools
python eval.py -dataset THUMOS14 -weight_file ../checkpoints/THUMOS14.pth

To evaluate BackTAL model on ActivityNet1.2, run:

cd ./tools
python eval.py -dataset ActivityNet1.2 -weight_file ../checkpoints/ActivityNet1.2.pth

To evaluate BackTAL model on HACS, run:

cd ./tools
python eval.py -dataset HACS -weight_file ../checkpoints/HACS.pth

Results

Our model achieves the following performance:

THUMOS14

threshold	0.3	0.4	0.5	0.6	0.7
mAP	54.4	45.5	36.3	26.2	14.8

ActivityNet v1.2

threshold	average-mAP	0.50	0.75	0.95
mAP	27.0	41.5	27.3	4.7

HACS

threshold	average-mAP	0.50	0.75	0.95
mAP	20.0	31.5	19.5	4.7

Training

To train the BackTAL model on THUMOS14 dataset, please run this command:

cd ./tools
python train.py -dataset THUMOS14

To train the BackTAL model on ActivityNet v1.2 dataset, please run this command:

cd ./tools
python train.py -dataset ActivityNet1.2

To train the BackTAL model on HACS dataset, please run this command:

cd ./tools
python train.py -dataset HACS

Citing BackTAL

@article{yang2021background,
  title={Background-Click Supervision for Temporal Action Localization},
  author={Yang, Le and Han, Junwei and Zhao, Tao and Lin, Tianwei and Zhang, Dingwen and Chen, Jianxin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}

Contact

For any discussions, please contact [e-mail].