rosebbb / Light-ASD

The repository for IEEE CVPR 2023 (A Light Weight Model for Active Speaker Detection)

A Light Weight Model for Active Speaker Detection

This repository contains the code and model weight for our paper (Accepted by CVPR 2023):

A Light Weight Model for Active Speaker Detection
Junhua Liao, Haihan Duan, Kanghui Feng, Wanbing Zhao, Yanbing Yang, Liangyin Chen

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Evaluate on AVA-Activespeaker dataset

Data preparation

Use the following code to download and preprocess the AVA dataset.

python train.py --dataPathAVA AVADataPath --download 

The AVA dataset and the labels will be downloaded into AVADataPath.

Training

You can train the model on the AVA dataset by using:

python train.py --dataPathAVA AVADataPath

exps/exps1/score.txt: output score file, exps/exp1/model/model_00xx.model: trained model, exps/exps1/val_res.csv: prediction for val set.

Testing

Our model weights have been placed in the weight folder. It performs mAP: 94.06% in the validation set. You can check it by using:

python train.py --dataPathAVA AVADataPath --evaluation

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Evaluate on Columbia ASD dataset

Testing

The model weights we trained on the AVA dataset have been placed in the weight folder. Then run the following code.

python Columbia_test.py --evalCol --colSavePath colDataPath

The Columbia ASD dataset and the labels will be downloaded into colDataPath. And you can get the following F1 result.

Name	Bell	Boll	Lieb	Long	Sick	Avg.
F1	82.7%	75.7%	87.0%	74.5%	85.4%	81.1%

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Citation

Please cite our paper if you use this code or model.

@inproceedings{liao2023light,
  title={A Light Weight Model for Active Speaker Detection},
  author={Liao, Junhua and Duan, Haihan and Feng, Kanghui and Zhao, Wanbing and Yang, Yanbing and Chen, Liangyin},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2023},
  organization={IEEE}
}

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Acknowledgments

Thanks for the support of TaoRuijie's open source repository for this research.