donghao51/MOOSA

[ECCV 2024] Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision

Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision

Hao Dong¹  Eleni Chatzi¹  Olga Fink²

¹ETH Zurich, ²EPFL

• ECCV 2024 •

---

Our proposed MOOSA framework for Multimodal Open-Set Domain Generalization and Adaptation.

Code

The code was tested using Python 3.10.13, torch 2.3.1+cu121 and NVIDIA GeForce RTX 3090, more dependencies are in requirement.txt.

Environments:

mmcv-full 1.2.7
mmaction2 0.13.0

EPIC-Kitchens Dataset

Prepare

Download Pretrained Weights

1. Download Audio model link, rename it as vggsound_avgpool.pth.tar and place under the EPIC-rgb-flow-audio/pretrained_models directory

2. Download SlowFast model for RGB modality link and place under the EPIC-rgb-flow-audio/pretrained_models directory

3. Download SlowOnly model for Flow modality link and place under the EPIC-rgb-flow-audio/pretrained_models directory

Download EPIC-Kitchens Dataset

bash download_script.sh 

Download Audio files EPIC-KITCHENS-audio.zip.

Unzip all files and the directory structure should be modified to match:

Click for details...

├── MM-SADA_Domain_Adaptation_Splits
├── rgb
|   ├── train
|   |   ├── D1
|   |   |   ├── P08_01.wav
|   |   |   ├── P08_01
|   |   |   |     ├── frame_0000000000.jpg
|   |   |   |     ├── ...
|   |   |   ├── P08_02.wav
|   |   |   ├── P08_02
|   |   |   ├── ...
|   |   ├── D2
|   |   ├── D3
|   ├── test
|   |   ├── D1
|   |   ├── D2
|   |   ├── D3


├── flow
|   ├── train
|   |   ├── D1
|   |   |   ├── P08_01 
|   |   |   |   ├── u
|   |   |   |   |   ├── frame_0000000000.jpg
|   |   |   |   |   ├── ...
|   |   |   |   ├── v
|   |   |   ├── P08_02
|   |   |   ├── ...
|   |   ├── D2
|   |   ├── D3
|   ├── test
|   |   ├── D1
|   |   ├── D2
|   |   ├── D3

Video and Audio

Click for details...

cd EPIC-rgb-flow-audio

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_audio -s D2 D3 -t D1 --lr 1e-4 --bsz 16 --nepochs 5 --mask_ratio 0.7 --entropy_min_weight 0.001 --datapath /path/to/EPIC-KITCHENS/

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_audio -s D1 D3 -t D2 --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.7 --entropy_min_weight 1.0 --datapath /path/to/EPIC-KITCHENS/

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_audio -s D1 D2 -t D3 --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.7 --entropy_min_weight 0.001 --datapath /path/to/EPIC-KITCHENS/

Video and Flow

Click for details...

cd EPIC-rgb-flow-audio

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_flow -s D2 D3 -t D1 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.7 --entropy_min_weight 0.001 --datapath /path/to/EPIC-KITCHENS/

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_flow -s D1 D3 -t D2 --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.7 --entropy_min_weight 1.0 --datapath /path/to/EPIC-KITCHENS/

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_flow -s D1 D2 -t D3 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.7 --entropy_min_weight 0.001 --datapath /path/to/EPIC-KITCHENS/

Flow and Audio

Click for details...

cd EPIC-rgb-flow-audio

python train_video_flow_audio_EPIC_MOOSA.py --use_flow --use_audio -s D2 D3 -t D1 --lr 1e-4 --bsz 16 --nepochs 25 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/EPIC-KITCHENS/

python train_video_flow_audio_EPIC_MOOSA.py --use_flow --use_audio -s D1 D3 -t D2 --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/EPIC-KITCHENS/

python train_video_flow_audio_EPIC_MOOSA.py --use_flow --use_audio -s D1 D2 -t D3 --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/EPIC-KITCHENS/

Video and Flow and Audio

Click for details...

cd EPIC-rgb-flow-audio

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_flow --use_audio -s D2 D3 -t D1 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.7 --entropy_min_weight 0.001 --jigsaw_num_splits 2 --datapath /path/to/EPIC-KITCHENS/

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_flow --use_audio -s D1 D3 -t D2 --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.7 --entropy_min_weight 0.1 --jigsaw_num_splits 2 --datapath /path/to/EPIC-KITCHENS/

python train_video_flow_audio_EPIC_MOOSA.py --use_video --use_flow --use_audio -s D1 D2 -t D3 --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.3 --entropy_min_weight 0.1 --jigsaw_num_splits 2  --datapath /path/to/EPIC-KITCHENS/

HAC Dataset

This dataset can be downloaded at link.

Unzip all files and the directory structure should be modified to match:

Click for details...

HAC
├── human
|   ├── videos
|   |   ├── ...
|   ├── flow
|   |   ├── ...
|   ├── audio
|   |   ├── ...

├── animal
|   ├── videos
|   |   ├── ...
|   ├── flow
|   |   ├── ...
|   ├── audio
|   |   ├── ...

├── cartoon
|   ├── videos
|   |   ├── ...
|   ├── flow
|   |   ├── ...
|   ├── audio
|   |   ├── ...

Download the pretrained weights similar to EPIC-Kitchens Dataset and put under the HAC-rgb-flow-audio/pretrained_models directory.

Video and Audio

Click for details...

cd HAC-rgb-flow-audio

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_audio -s 'animal' 'cartoon' -t 'human' --lr 1e-4 --bsz 16 --nepochs 5 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/HAC/

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_audio -s 'human' 'cartoon' -t 'animal' --lr 1e-4 --bsz 16  --nepochs 10 --mask_ratio 0.7 --entropy_min_weight 0.001 --datapath /path/to/HAC/

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_audio -s 'human' 'animal' -t 'cartoon' --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/HAC/

Video and Flow

Click for details...

cd HAC-rgb-flow-audio

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_flow -s 'animal' 'cartoon' -t 'human' --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/HAC/

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_flow -s 'human' 'cartoon' -t 'animal' --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/HAC/

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_flow -s 'human' 'animal' -t 'cartoon' --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/HAC/

Flow and Audio

Click for details...

cd HAC-rgb-flow-audio

python train_video_flow_audio_HAC_MOOSA.py --use_flow --use_audio -s 'animal' 'cartoon' -t 'human' --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/HAC/

python train_video_flow_audio_HAC_MOOSA.py --use_flow --use_audio -s 'human' 'cartoon' -t 'animal' --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.3 --entropy_min_weight 0.001 --datapath /path/to/HAC/

python train_video_flow_audio_HAC_MOOSA.py --use_flow --use_audio -s 'human' 'animal' -t 'cartoon' --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.7 --entropy_min_weight 0.001 --datapath /path/to/HAC/

Video and Flow and Audio

Click for details...

cd HAC-rgb-flow-audio

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_flow --use_audio -s 'animal' 'cartoon' -t 'human' --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.7 --entropy_min_weight 0.001 --jigsaw_num_splits 2 --datapath /path/to/HAC/

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_flow --use_audio -s 'human' 'cartoon' -t 'animal' --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.7 --entropy_min_weight 0.001 --jigsaw_num_splits 2 --jigsaw_samples 64 --datapath /path/to/HAC/

python train_video_flow_audio_HAC_MOOSA.py --use_video --use_flow --use_audio -s 'human' 'animal' -t 'cartoon' --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.7 --alpha_trans 0.5 --entropy_min_weight 0.001 --jigsaw_num_splits 2 --datapath /path/to/HAC/

Multimodal Open-Set Domain Adaptation

Video and Audio

Click for details...

cd EPIC-rgb-flow-audio

python train_video_audio_EPIC_MOOSA_OSDA.py -s D1 D2 -t D2 --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.3 --target_filter_thr 0.5 --datapath /path/to/EPIC-KITCHENS/

python train_video_audio_EPIC_MOOSA_OSDA.py -s D1 D3 -t D3 --lr 1e-4 --bsz 16 --nepochs 10 --mask_ratio 0.7 --target_filter_thr 0.5 --datapath /path/to/EPIC-KITCHENS/

python train_video_audio_EPIC_MOOSA_OSDA.py -s D2 D1 -t D1 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.7 --target_filter_thr 0.5 --datapath /path/to/EPIC-KITCHENS/

python train_video_audio_EPIC_MOOSA_OSDA.py -s D2 D3 -t D3 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.7 --target_filter_thr 0.5 --datapath /path/to/EPIC-KITCHENS/

python train_video_audio_EPIC_MOOSA_OSDA.py -s D3 D1 -t D1 --lr 1e-4 --bsz 16 --nepochs 20 --mask_ratio 0.7 --target_filter_thr 0.3 --datapath /path/to/EPIC-KITCHENS/

python train_video_audio_EPIC_MOOSA_OSDA.py -s D3 D2 -t D2 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.7 --target_filter_thr 0.5 --datapath /path/to/EPIC-KITCHENS/

Multimodal Open-Partial Domain Generalization

Video and Audio

Click for details...

cd EPIC-rgb-flow-audio

python train_video_audio_EPIC_MOOSA_Open_Partial.py -s D2 D3 -t D1 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.7 --entropy_min_weight 1.0 --datapath /path/to/EPIC-KITCHENS/

python train_video_audio_EPIC_MOOSA_Open_Partial.py -s D1 D3 -t D2 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.7 --entropy_min_weight 1.0 --datapath /path/to/EPIC-KITCHENS/

python train_video_audio_EPIC_MOOSA_Open_Partial.py -s D1 D2 -t D3 --lr 1e-4 --bsz 16 --nepochs 15 --mask_ratio 0.3 --entropy_min_weight 1.0 --datapath /path/to/EPIC-KITCHENS/

Contact

If you have any questions, please send an email to donghaospurs@gmail.com

Citation

If you find our work useful in your research please consider citing our paper:

@inproceedings{dong2024moosa,
    title={Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision},
    author={Dong, Hao and Chatzi, Eleni and Fink, Olga},
    booktitle={European Conference on Computer Vision},
    year={2024}
}

Related Projects

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities

Acknowledgement

Many thanks to the excellent open-source projects SimMMDG and DomainAdaptation.