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.

Code

https://github.com/donghao51/MultiOOD

MultiOOD Benchmark

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

Prepare Datasets

1. 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:

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


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

2. 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:

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


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

3. 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:

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

4. Download HAC video, audio and optical flow data from link and extract. The directory structure should be modified to match:

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

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

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

5. 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:

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 in https://github.com/donghao51/MultiOOD 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.

Contact

If you have any questions, please send an email to [email protected]

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},
}