Localizing Visual Sounds the Easy Way

Official codebase for EZ-VSL. EZ-VSL is a simple yet effective approach for Visual Sound Localization. Please check out the paper for full details.

Localizing Visual Sounds the Easy Way
Shentong Mo, Pedro Morgado
arXiv 2022.

Environment

To setup the environment, please simply run

pip install -r requirements.txt

Datasets

Flickr-SoundNet

Data can be downloaded from Learning to localize sound sources

VGG-Sound Source

Data can be downloaded from Localizing Visual Sounds the Hard Way

VGG-SS Unheard & Heard Test Data

Data can be downloaded from Unheard and Heard

Model Zoo

We release several models pre-trained with EZ-VSL with the hope that other researchers might also benefit from them.

Method	Train Set	Test Set	CIoU	AUC	url	Train	Test
EZ-VSL	Flickr 10k	Flickr SoundNet	81.93	62.58	model	script	script
EZ-VSL	Flickr 144k	Flickr SoundNet	83.13	63.06	model	script	script
EZ-VSL	VGG-Sound 144k	Flickr SoundNet	83.94	63.60	model	script	script
EZ-VSL	VGG-Sound 10k	VGG-SS	37.18	38.75	model	script	script
EZ-VSL	VGG-Sound 144k	VGG-SS	38.85	39.54	model	script	script
EZ-VSL	VGG-Sound Full	VGG-SS	39.34	39.78	model	script	script
EZ-VSL	Heard 110	Heard 110	37.25	38.97	model	script	script
EZ-VSL	Heard 110	Unheard 110	39.57	39.60	model	script	script

Train & Test

For training an EZ-VSL model, please run

python train.py --multiprocessing_distributed \
    --train_data_path /path/to/Flickr-all/ \
    --test_data_path /path/to/Flickr-SoundNet/ \
    --test_gt_path /path/to/Flickr-SoundNet/Annotations/ \
    --experiment_name flickr_10k \
    --trainset 'flickr_10k' \
    --testset 'flickr' \
    --epochs 100 \
    --batch_size 128 \
    --init_lr 0.0001

For testing and visualization, simply run

python test.py --test_data_path /path/to/Flickr-SoundNet/ \
    --test_gt_path /path/to/Flickr-SoundNet/Annotations/ \
    --model_dir checkpoints \
    --experiment_name flickr_10k \
    --save_visualizations \
    --testset 'flickr' \
    --alpha 0.4

The training script supports the following training sets: flickr, flickr_10k, flickr_144k, vggss, vggss_10k, vggss_144k or vggss_heard.

For evaluation, it supports the following test sets: flickr, vggss, vggss_heard, vggss_unheard.

Visualizations

The test.py script saves the predicted localization maps for all test images when the flag --save_visualizations is provided. All visualizations for OGL, AVL and EZ-VSL localization maps are saved under {model_dir}/{experiment_name}/viz/. Here's some examples.

Citation

If you find this repository useful, please cite our paper:

@article{mo2022EZVSL,
  title={Localizing Visual Sounds the Easy Way},
  author={Mo, Shentong and Morgado, Pedro},
  journal={arXiv preprint arXiv:2203.09324},
  year={2022}
}

stoneMo/EZ-VSL