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

Hao Dong¹ Yue Zhao² Eleni Chatzi¹ Olga Fink³

¹ETH Zurich, ²University of Southern California, ³EPFL

• arXiv •

MultiOOD is the first-of-its-kind benchmark for Multimodal OOD Detection, characterized by diverse dataset sizes and varying modality combinations.

MultiOOD Benchmark

MultiOOD is based on five public action recognition datasets (HMDB51, UCF101, EPIC-Kitchens, HAC, and Kinetics-600).

Prepare Datasets

Download HMDB51 video data from link and extract. Download HMDB51 optical flow data from link and extract. The directory structure should be modified to match:

Click for details...

HMDB51
├── video
|   ├── catch
|   |   ├── *.avi
|   ├── climb
|   |   ├── *.avi
|   |── ...


├── flow
|   ├── *_flow_x.mp4
|   ├── *_flow_y.mp4
|   ├── ...

Download UCF101 video data from link and extract. Download UCF101 optical flow data from link and extract. The directory structure should be modified to match:

Click for details...

UCF101
├── video
|   ├── *.avi
|   |── ...


├── flow
|   ├── *_flow_x.mp4
|   ├── *_flow_y.mp4
|   ├── ...

Download EPIC-Kitchens video and optical flow data by

bash utils/download_epic_script.sh

Download audio data from link.

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

Click for details...

EPIC-KITCHENS
├── rgb
|   ├── train
|   |   ├── D3
|   |   |   ├── P22_01.wav
|   |   |   ├── P22_01
|   |   |   |     ├── frame_0000000000.jpg
|   |   |   |     ├── ...
|   |   |   ├── P22_02
|   |   |   ├── ...
|   ├── test
|   |   ├── D3


├── flow
|   ├── train
|   |   ├── D3
|   |   |   ├── P22_01
|   |   |   |     ├── frame_0000000000.jpg
|   |   |   |     ├── ...
|   |   |   ├── P22_02
|   |   |   ├── ...
|   ├── test
|   |   ├── D3

Download HAC video, audio and optical flow data from link and extract. 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 Kinetics-600 video data by

wget -i utils/filtered_k600_train_path.txt

Extract all files and get audio data from video data by

python utils/generate_audio_files.py

Download Kinetics-600 optical flow data (kinetics600_flow_mp4_part_*) from link and extract (run cat kinetics600_flow_mp4_part_* > kinetics600_flow_mp4.tar.gz and then tar -zxvf kinetics600_flow_mp4.tar.gz).

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

Click for details...

Kinetics-600
├── video
|   ├── acting in play
|   |   ├── *.mp4
|   |   ├── *.wav
|   |── ...


├── flow
|   ├── acting in play
|   |   ├── *_flow_x.mp4
|   |   ├── *_flow_y.mp4
|   ├── ...

Dataset Splits

The splits for Multimodal Near-OOD and Far-OOD Benchmarks are provided under HMDB-rgb-flow/splits/ for HMDB51, UCF101, HAC, and Kinetics-600, and under EPIC-rgb-flow/splits/ for EPIC-Kitchens.

Methodology

An overview of the proposed framework for Multimodal OOD Detection. We introduce A2D algorithm to encourage enlarging the prediction discrepancy across modalities. Additionally, we propose a novel outlier synthesis algorithm, NP-Mix, designed to explore broader feature spaces, which complements A2D to strengthen the OOD detection performance.

Code

The code was tested using Python 3.10.4, torch 1.11.0+cu113 and NVIDIA GeForce RTX 3090. More dependencies are in requirement.txt.

Prepare

Download Pretrained Weights

Download SlowFast model for RGB modality link and place under the HMDB-rgb-flow/pretrained_models and EPIC-rgb-flow/pretrained_models directory
Download SlowOnly model for Flow modality link and place under the HMDB-rgb-flow/pretrained_models and EPIC-rgb-flow/pretrained_models directory
Download Audio model link, rename it as vggsound_avgpool.pth.tar and place under the HMDB-rgb-flow/pretrained_models and EPIC-rgb-flow/pretrained_models directory

Multimodal Near-OOD Benchmark

HMDB51 25/26

Click for details...

cd HMDB-rgb-flow/

Train the Near-OOD baseline model for HMDB:

python train_video_flow.py --near_ood --dataset 'HMDB' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/HMDB51/'

Train the Near-OOD model using A2D for HMDB:

python train_video_flow.py --near_ood --dataset 'HMDB' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 1.0 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/HMDB51/'

Train the Near-OOD model using A2D and NP-Mix for HMDB:

python train_video_flow.py --near_ood --dataset 'HMDB' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 0.5 --use_npmix --max_ood_hellinger --a2d_ratio_ood 0.5 --ood_entropy_ratio 0.5 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/HMDB51/'

You can also download our provided checkpoints (HMDB_near_ood_baseline.pt, HMDB_near_ood_a2d.pt, and HMDB_near_ood_a2d_npmix.pt) from link.

Save the evaluation files for HMDB (to save evaluation files for ASH or ReAct, you should also run following line with options --use_ash or --use_react):

python test_video_flow.py --bsz 16 --num_workers 2 --near_ood --dataset 'HMDB' --appen 'a2d_npmix_best_' --resumef '/path/to/HMDB_near_ood_a2d_npmix.pt'

Evaluation for HMDB (change --postprocessor to different score functions):

python eval_video_flow_near_ood.py --postprocessor msp --appen 'a2d_npmix_best_' --dataset 'HMDB' --path 'HMDB-rgb-flow/'

UCF101 50/51

Click for details...

cd HMDB-rgb-flow/

Train the Near-OOD baseline model for UCF:

python train_video_flow.py --near_ood --dataset 'UCF' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/UCF101/'

Train the Near-OOD model using A2D for UCF:

python train_video_flow.py --near_ood --dataset 'UCF' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 1.0 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/UCF101/'

Train the Near-OOD model using A2D and NP-Mix for UCF:

python train_video_flow.py --near_ood --dataset 'UCF' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 0.5 --use_npmix --max_ood_hellinger --a2d_ratio_ood 0.5 --ood_entropy_ratio 0.5 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/UCF101/'

You can also download our provided checkpoints (UCF_near_ood_baseline.pt, UCF_near_ood_a2d.pt, and UCF_near_ood_a2d_npmix.pt) from link.

Save the evaluation files for UCF (to save evaluation files for ASH or ReAct, you should also run following line with options --use_ash or --use_react):

python test_video_flow.py --bsz 16 --num_workers 2 --near_ood --dataset 'UCF' --appen 'a2d_npmix_best_' --resumef '/path/to/UCF_near_ood_a2d_npmix.pt'

Evaluation for UCF (change --postprocessor to different score functions):

python eval_video_flow_near_ood.py --postprocessor msp --appen 'a2d_npmix_best_' --dataset 'UCF' --path 'HMDB-rgb-flow/'

EPIC-Kitchens 4/4

Click for details...

cd EPIC-rgb-flow/

Train the Near-OOD baseline model for EPIC:

python train_video_flow_epic.py --dataset 'EPIC' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/EPIC-Kitchens/'

Train the Near-OOD model using A2D for EPIC:

python train_video_flow_epic.py --dataset 'EPIC' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 1.0 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/EPIC-Kitchens/'

Train the Near-OOD model using A2D and NP-Mix for EPIC:

python train_video_flow_epic.py --dataset 'EPIC' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 0.1 --use_npmix --max_ood_hellinger --a2d_ratio_ood 0.1 --ood_entropy_ratio 0.1 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/EPIC-Kitchens/'

You can also download our provided checkpoints (EPIC_near_ood_baseline.pt, EPIC_near_ood_a2d.pt, and EPIC_near_ood_a2d_npmix.pt) from link.

Save the evaluation files for EPIC (to save evaluation files for ASH or ReAct, you should also run following line with options --use_ash or --use_react):

python test_video_flow_epic.py --bsz 16 --num_workers 2  --ood_dataset 'EPIC' --appen 'a2d_npmix_best_' --resumef '/path/to/EPIC_near_ood_a2d_npmix.pt'

Evaluation for EPIC (change --postprocessor to different score functions):

python eval_video_flow_near_ood.py --postprocessor msp --appen 'a2d_npmix_best_' --dataset 'EPIC' --path 'EPIC-rgb-flow/'

Kinetics-600 129/100

Click for details...

cd HMDB-rgb-flow/

Train the Near-OOD baseline model for Kinetics:

python train_video_flow.py --near_ood --dataset 'Kinetics' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/Kinetics-600/'

Train the Near-OOD model using A2D for Kinetics:

python train_video_flow.py --near_ood --dataset 'Kinetics' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 1.0 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/Kinetics-600/'

Train the Near-OOD model using A2D and NP-Mix for Kinetics:

python train_video_flow.py --near_ood --dataset 'Kinetics' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 0.1 --use_npmix --max_ood_hellinger --a2d_ratio_ood 0.1 --ood_entropy_ratio 0.1 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/Kinetics-600/'

You can also download our provided checkpoints (Kinetics_near_ood_baseline.pt, Kinetics_near_ood_a2d.pt, and Kinetics_near_ood_a2d_npmix.pt) from link.

Save the evaluation files for Kinetics (to save evaluation files for ASH or ReAct, you should also run following line with options --use_ash or --use_react):

python test_video_flow.py --bsz 16 --num_workers 2 --near_ood --dataset 'Kinetics' --appen 'a2d_npmix_best_' --resumef '/path/to/Kinetics_near_ood_a2d_npmix.pt'

Evaluation for Kinetics (change --postprocessor to different score functions):

python eval_video_flow_near_ood.py --postprocessor msp --appen 'a2d_npmix_best_' --dataset 'Kinetics' --path 'HMDB-rgb-flow/'

Multimodal Far-OOD Benchmark

HMDB51 as ID

Click for details...

cd HMDB-rgb-flow/

Train the Far-OOD baseline model for HMDB:

python train_video_flow.py --dataset 'HMDB' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/HMDB51/'

Train the Far-OOD model using A2D and NP-Mix for HMDB:

python train_video_flow.py --dataset 'HMDB' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 0.1 --use_npmix --max_ood_hellinger --a2d_ratio_ood 0.1 --ood_entropy_ratio 0.1 --nepochs 50 --appen '' --save_best --save_checkpoint --datapath '/path/to/HMDB51/'

You can also download our provided checkpoints (HMDB_far_ood_baseline.pt and HMDB_far_ood_a2d_npmix.pt) from link.

Save the evaluation files for HMDB (to save evaluation files for ASH or ReAct, you should also run following line with options --use_ash or --use_react, same for other datasets):

python test_video_flow.py --bsz 16 --num_workers 2 --dataset 'HMDB' --appen 'a2d_npmix_best_' --resumef '/path/to/HMDB_far_ood_a2d_npmix.pt'

Save the evaluation files for UCF:

python test_video_flow.py --bsz 16 --num_workers 2 --far_ood --dataset 'HMDB' --ood_dataset 'UCF' --appen 'a2d_npmix_best_' --resumef '/path/to/HMDB_far_ood_a2d_npmix.pt'

Save the evaluation files for HAC:

python test_video_flow.py --bsz 16 --num_workers 2 --far_ood --dataset 'HMDB' --ood_dataset 'HAC' --appen 'a2d_npmix_best_' --resumef '/path/to/HMDB_far_ood_a2d_npmix.pt'

Save the evaluation files for Kinetics:

python test_video_flow.py --bsz 16 --num_workers 2 --far_ood --dataset 'HMDB' --ood_dataset 'Kinetics' --appen 'a2d_npmix_best_' --resumef '/path/to/HMDB_far_ood_a2d_npmix.pt'

Save the evaluation files for EPIC:

cd EPIC-rgb-flow/

python test_video_flow_epic.py --bsz 16 --num_workers 2 --far_ood --dataset 'HMDB' --ood_dataset 'EPIC' --appen 'a2d_npmix_best_' --resumef '/path/to/HMDB_far_ood_a2d_npmix.pt'

Evaluation for UCF (change --postprocessor to different score functions, change --ood_dataset to UCF, EPIC, HAC, or Kinetics):

python eval_video_flow_far_ood.py --postprocessor msp --appen 'a2d_npmix_best_' --dataset 'HMDB' --ood_dataset 'UCF' --path 'HMDB-rgb-flow/'

Kinetics as ID

Click for details...

cd HMDB-rgb-flow/

Train the Far-OOD baseline model for Kinetics:

python train_video_flow.py --dataset 'Kinetics' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/Kinetics-600/'

Train the Far-OOD model using A2D and NP-Mix for Kinetics:

python train_video_flow.py --dataset 'Kinetics' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 0.1 --use_npmix --max_ood_hellinger --a2d_ratio_ood 0.1 --ood_entropy_ratio 0.1 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/Kinetics-600/'

You can also download our provided checkpoints (Kinetics_far_ood_baseline.pt and Kinetics_far_ood_a2d_npmix.pt) from link.

Save the evaluation files for Kinetics (to save evaluation files for ASH or ReAct, you should also run following line with options --use_ash or --use_react, same for other datasets):

python test_video_flow.py --bsz 16 --num_workers 2 --dataset 'Kinetics' --appen 'a2d_npmix_best_' --resumef '/path/to/Kinetics_far_ood_a2d_npmix.pt'

Save the evaluation files for HMDB:

python test_video_flow.py --bsz 16 --num_workers 2 --far_ood --dataset 'Kinetics' --ood_dataset 'HMDB' --appen 'a2d_npmix_best_' --resumef '/path/to/Kinetics_far_ood_a2d_npmix.pt'

Save the evaluation files for UCF:

python test_video_flow.py --bsz 16 --num_workers 2 --far_ood --dataset 'Kinetics' --ood_dataset 'UCF' --appen 'a2d_npmix_best_' --resumef '/path/to/Kinetics_far_ood_a2d_npmix.pt'

Save the evaluation files for HAC:

python test_video_flow.py --bsz 16 --num_workers 2 --far_ood --dataset 'Kinetics' --ood_dataset 'HAC' --appen 'a2d_npmix_best_' --resumef '/path/to/cKinetics_far_ood_a2d_npmix.pt'

Save the evaluation files for EPIC:

cd EPIC-rgb-flow/

python test_video_flow_epic.py --bsz 16 --num_workers 2 --far_ood --dataset 'Kinetics' --ood_dataset 'EPIC' --appen 'a2d_npmix_best_' --resumef '/path/to/Kinetics_far_ood_a2d_npmix.pt'

Evaluation for UCF (change --postprocessor to different score functions, change --ood_dataset to UCF, EPIC, HAC, or HMDB):

python eval_video_flow_far_ood.py --postprocessor msp --appen 'a2d_npmix_best_' --dataset 'Kinetics' --ood_dataset 'UCF' --path 'HMDB-rgb-flow/'

Multimodal Near-OOD Benchmark with Video, Audio, and Optical Flow

EPIC-Kitchens 4/4

Click for details...

cd EPIC-rgb-flow/

Train the Near-OOD baseline model for EPIC:

python train_video_flow_audio_epic.py --dataset 'EPIC' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/EPIC-Kitchens/'

Train the Near-OOD model using A2D and NP-Mix for EPIC:

python train_video_flow_audio_epic.py --dataset 'EPIC' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 0.5 --use_npmix --max_ood_hellinger --a2d_ratio_ood 0.5 --ood_entropy_ratio 0.5 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/EPIC-Kitchens/'

You can also download our provided checkpoints (EPIC_near_ood_vfa_baseline.pt and EPIC_near_ood_vfa_a2d_npmix.pt) from link.

Save the evaluation files for EPIC (to save evaluation files for ASH or ReAct, you should also run following line with options --use_ash or --use_react):

python test_video_flow_audio_epic.py --bsz 16 --num_workers 2  --ood_dataset 'EPIC' --appen 'a2d_npmix_best_' --resumef '/path/to/EPIC_near_ood_vfa_a2d_npmix.pt'

Evaluation for EPIC (change --postprocessor to different score functions):

python eval_video_flow_near_ood.py --postprocessor msp --appen 'vfa_a2d_npmix_best_' --dataset 'EPIC' --path 'EPIC-rgb-flow/'

Kinetics-600 129/100

Click for details...

cd HMDB-rgb-flow/

Train the Near-OOD baseline model for Kinetics:

python train_video_flow_audio.py --near_ood --dataset 'Kinetics' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/Kinetics-600/'

Train the Near-OOD model using A2D and NP-Mix for Kinetics:

python train_video_flow_audio.py --near_ood --dataset 'Kinetics' --lr 0.0001 --seed 0 --bsz 16 --num_workers 10 --start_epoch 10 --use_single_pred --use_a2d --a2d_max_hellinger --a2d_ratio 0.5 --use_npmix --max_ood_hellinger --a2d_ratio_ood 0.5 --ood_entropy_ratio 0.5 --nepochs 20 --appen '' --save_best --save_checkpoint --datapath '/path/to/Kinetics-600/'

You can also download our provided checkpoints (Kinetics_near_ood_vfa_baseline.pt and Kinetics_near_ood_vfa_a2d_npmix.pt) from link.

Save the evaluation files for Kinetics (to save evaluation files for ASH or ReAct, you should also run following line with options --use_ash or --use_react):

python test_video_flow_audio.py --bsz 16 --num_workers 2 --near_ood --dataset 'Kinetics' --appen 'a2d_npmix_best_' --resumef '/path/to/Kinetics_near_ood_a2d_npmix.pt'

Evaluation for Kinetics (change --postprocessor to different score functions):

python eval_video_flow_near_ood.py --postprocessor msp --appen 'vfa_a2d_npmix_best_' --dataset 'Kinetics' --path 'HMDB-rgb-flow/'

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:

@article{dong2024multiood,
	author   = {Hao Dong and Yue Zhao and Eleni Chatzi and Olga Fink},
	title    = {{MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities}},
    journal  = {arXiv preprint arXiv:2405.17419},
	year     = {2024},
}

Related Projects

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

Acknowledgement

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

donghao51 / MultiOOD

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

• arXiv •

MultiOOD Benchmark

Prepare Datasets

Dataset Splits

Methodology

Code

Prepare

Download Pretrained Weights

Multimodal Near-OOD Benchmark

HMDB51 25/26

UCF101 50/51

EPIC-Kitchens 4/4

Kinetics-600 129/100

Multimodal Far-OOD Benchmark

HMDB51 as ID

Kinetics as ID

Multimodal Near-OOD Benchmark with Video, Audio, and Optical Flow

EPIC-Kitchens 4/4

Kinetics-600 129/100

Contact

Citation

Related Projects

Acknowledgement

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