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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

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.

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.

Download our model weight (coming soon) and place it in the weight folder. It performs mAP: 94.1% in the validation set. You can check it by using:

python train.py --dataPathAVA AVADataPath --evaluation

Download the model weight (coming soon) we trained on the AVA dataset and place it 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.

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}
}

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