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This repository provides the PyTorch implementation for the ECCV2022 paper "Audio-Visual Segmentation". This paper proposes the audio-visual segmentation problem and the AVSBench dataset accordingly.

• We are preparing the AVSBench-V2 which is 10 times larger than AVSBench. AVSBench-V2 will pay more attention to multi-source situation.

• AVSBench dataset

The csv file that contains the video ids for downloading the raw YouTube videos and the annoated ground truth segmentation maps can be downloaded from here.

There are two ways to obtain the video data:

1. Please send an email to [email protected] for the processed videos and audios, with your name and institution.
2. We also provide some scripts to process the raw video data and extract the frames/mel-spectrogram features.

cd preprocess_scripts
python preprocess_s4.py # for Single-source set
python preprocess_ms3.py # for Multi-sources set

The data should be placed to the directory avsbench_data.

• pretrained backbone

The pretrained ResNet50/PVT-v2-b5 (vision) and VGGish (audio) backbones can be downloaded from here and placed to the directory pretrained_backbones.

Notice: please update the path of data and pretrained backbone in avs_s4/config.py and avs_ms3/config.py accordingly.

• Train AVS Model

cd avs_scripts/avs_s4
bash train.sh

• Test AVS Model

cd avs_scripts/avs_s4
bash test.sh

• Train AVS Model

cd avs_scripts/avs_ms3
bash train.sh

• Test AVS Model

cd avs_scripts/avs_ms3
bash test.sh

If you use this dataset or code, please consider cite:

@inproceedings{zhou2022avs,
  title     = {Audio-Visual Segmentation},
  author    = {Zhou, Jinxing and Wang, Jianyuan and Zhang, Jiayi and Sun, Weixuan and Zhang, Jing and Birchfield, Stan and Guo, Dan and Kong, Lingpeng and Wang, Meng and Zhong, Yiran},
  booktitle = {European Conference on Computer Vision},
  year      = {2022}
}

This project is released under the Apache 2.0 license as found in the LICENSE file.