Awesome

AVSA

This repository contains the code and dataset in our NeurIPS'20 paper.

Learning Representations from Audio-Visual Spatial Alignment. Pedro Morgado*, Yi Li*, Nuno Vasconcelos. Advances in Neural Information Processing Systems (NeurIPS), 2020.

Prerequisites

Requirements listed in environment.yml.

Data preparation

YT-360 dataset

YouTube id's of videos in the YT-360 dataset are provided in datasets/assets/yt360/[train|test].txt, and segment timestamps in datasets/assets/yt360/segments.txt. Please, use your favorite YouTube dataset downloader to download the videos (e.g.~link), and split them into 10s clips. The dataset should be stored in data/yt360/video and data/yt360/audio with filenames {YOUTUBE_ID}-{SEGMENT_START_TIME}.{EXTENSION}.

The pre-extracted segmentation maps can be downloaded from here and extracted to data/yt360/segmentation/.

If you experience issues downloading or processing the dataset, please email the authors at {pmaravil, yil898}@eng.ucsd.edu for assistance.

Pre-trained model

The AVSA model that yield the top performance (trained from configs/main/avsa/Cur-Loc4-TransfD2.yaml) is available here.

Self-supervised training

python main-video-ssl.py [--quiet] cfg

Training config cfg for the following models are provided:

• AVC training (instance discrimination): configs/main/avsa/InstDisc.yaml
• AVSA training: configs/main/avsa/Cur-Loc4-TransfD2.yaml
• AVSA training w/o curriculum: configs/main/avsa/NoCur-Loc4-TransfD2.yaml

Evaluation

Four downstream tasks are supported: Binary audio-visual correspondence (AVC-Bin), binary audio-visual spatial alignment (AVSA-Bin), video action recognition (on UCF/HMDB), and audio-visual semantic segmentation.

Action recognition

python eval-action-recg.py [--quiet] cfg model_cfg

Evaluation config cfg for UCF and HMDB dataset are provided:

• UCF: configs/benchmark/ucf/ucf-8at16-fold[1|2|3].yaml
• HMDB: configs/benchmark/hmdb/hmdb-8at16-fold[1|2|3].yaml

model_cfg is training config for the model to evaluate, e.g. configs/main/avsa/Cur-Loc4-TransfD2.yaml for AVSA pre-training.

Semantic segmentation

python eval-audiovisual-segm.py [--quiet] cfg model_cfg

Evaluation config cfg for three settings are provided:

• Visual segmentation: configs/benchmark/segmentation/yt360-fpn-4crop-head-vonly.yaml
• Visual+audio segmentation: configs/benchmark/segmentation/yt360-fpn-4crop-head-audio.yaml
• Visual+audio segmentation with context: configs/benchmark/segmentation/yt360-fpn-4crop-head-audio-ctx.yaml

Binary audio-visual correspondence

python eval-avc.py [--quiet] cfg model_cfg

Evaluation config cfg for two settings are provided:

• With transformer: configs/benchmark/avc/avc-transf-[1|4]crop.yaml
• Without transformer: configs/benchmark/avc/avc-notransf-[1|4]crop.yaml

Binary audio-visual spatial alignment

python eval-avsa.py [--quiet] cfg model_cfg

Evaluation config cfg for two settings are provided:

• With transformer: configs/benchmark/avsa/avsa-transf-[1|4]crop.yaml
• Without transformer: configs/benchmark/avsa/avsa-notransf-[1|4]crop.yaml

Citations

Please cite our work if you find it helpful for your research:

@article{morgado2020learning,
  title={Learning Representations from Audio-Visual Spatial Alignment},
  author={Morgado, Pedro and Li, Yi and Nvasconcelos, Nuno},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Acknowledgements

This work was partially funded by NSF award IIS-1924937 and NVIDIA GPU donations. We also acknowledge and thank the use of the Nautilus platform for some of the experiments in paper.