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Ref-AVS

The official repo for "Ref-AVS: Refer and Segment Objects in Audio-Visual Scenes", ECCV 2024

Project Page

Dataset Download

>>> Introduction

In this paper, we propose a pixel-level segmentation task called Referring Audio-Visual Segmentation (Ref-AVS), which requires the network to densely predict whether each pixel corresponds to the given multimodal-cue expression, including dynamic audio-visual information.

• Top-left of Fig.1 highlights the distinctions between Ref-AVS and previous tasks.

• Fig.2 shows the proposed baseline model to process multimodal-cues.

• Fig.3 shows the statistics of this dataset.

>>> Run

Run the training & evaluation:

cd Ref_AVS
sh run.sh  # you should change your path configs. See /configs/config.py for more details.

You can download the checkpoint here.

Core dependencies:

transformers=4.30.2
towhee=1.1.3
towhee-models=1.1.3  # Towhee is used for extracting VGGish audio feature.

>>> FAQ

(1) Alternative Audio Feature Extraction

If you found the towhee is hard to establish, please consider using the following code with Google CoLab: link.

Citation

If you find this work useful, please consider citing it:

@article{wang2024refavs,
  title={Ref-AVS: Refer and Segment Objects in Audio-Visual Scenes},
  author={Wang, Yaoting and Sun, Peiwen and Zhou, Dongzhan and Li, Guangyao and Zhang, Honggang and Hu, Di},
  journal={IEEE European Conference on Computer Vision (ECCV)},
  year={2024},
}

@inproceedings{wang2024prompting,
  title={Prompting segmentation with sound is generalizable audio-visual source localizer},
  author={Wang, Yaoting and Liu, Weisong and Li, Guangyao and Ding, Jian and Hu, Di and Li, Xi},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={6},
  pages={5669--5677},
  year={2024}
}