Code and Dataset for "Localizing Visual Sounds the Hard Way".
The repo contains code and our pre-trained model.
- Python 3.6.8
- Pytorch 1.3.0
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"
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
@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"}
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