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Localizing-Visual-Sounds-the-Hard-Way

Code and Dataset for "Localizing Visual Sounds the Hard Way".

The repo contains code and our pre-trained model.

Environment

• Python 3.6.8
• Pytorch 1.3.0

Flickr-SoundNet

We provide the pretrained model here.

To test the model, testing data and ground truth should be downloaded from learning to localize sound source.

Then run

python test.py --data_path "path to downloaded data with structure below/" --summaries_dir "path to pretrained models" --gt_path "path to ground truth" --testset "flickr"

VGG-Sound Source

We provide the pretrained model here.

To test the model, run

python test.py --data_path "path to downloaded data with structure below/" --summaries_dir "path to pretrained models" --testset "vggss"

(Note, some gt bounding boxes are updated recently, all results on VGG-SS cause a 2~3% difference on IoU.)

Both test data should be placed in the following structure.

data path
│
└───frames
│   │   image001.jpg
│   │   image002.jpg
│   │
└───audio
    │   audio011.wav
    │   audio012.wav

Citation

@InProceedings{Chen21,
              title        = "Localizing Visual Sounds the Hard Way",
              author       = "Honglie Chen, Weidi Xie, Triantafyllos Afouras, Arsha Nagrani, Andrea Vedaldi, Andrew Zisserman",
              booktitle    = "CVPR",
              year         = "2021"}