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--- |
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license: apache-2.0 |
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task_categories: |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- Out-of-Distribution Detection |
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- Multimodal Learning |
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pretty_name: MultiOOD |
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size_categories: |
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- 100K<n<1M |
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--- |
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<div align="center"> |
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<h1>MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities</h1> |
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<div> |
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<a href='https://sites.google.com/view/dong-hao/' target='_blank'>Hao Dong</a><sup>1</sup> |
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  |
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<a href='https://viterbi-web.usc.edu/~yzhao010/' target='_blank'>Yue Zhao</a><sup>2</sup> |
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  |
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<a href='https://chatzi.ibk.ethz.ch/about-us/people/prof-dr-eleni-chatzi.html' target='_blank'>Eleni Chatzi</a><sup>1</sup> |
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  |
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<a href='https://people.epfl.ch/olga.fink?lang=en' target='_blank'>Olga Fink</a><sup>3</sup> |
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</div> |
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<div> |
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<sup>1</sup>ETH Zurich, <sup>2</sup>University of Southern California, <sup>3</sup>EPFL |
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</div> |
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<div> |
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<h4 align="center"> |
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• <a href="https://arxiv.org/abs/2405.17419" target='_blank'>arXiv</a> • |
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</h4> |
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</div> |
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<div style="text-align:center"> |
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<img src="multiood.jpg" width="100%" height="100%"> |
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</div> |
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--- |
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</div> |
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MultiOOD is the first-of-its-kind benchmark for Multimodal OOD Detection, characterized by diverse dataset sizes and varying modality combinations. |
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## Code |
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https://github.com/donghao51/MultiOOD |
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## MultiOOD Benchmark |
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MultiOOD is based on five public action recognition datasets (HMDB51, UCF101, EPIC-Kitchens, HAC, and Kinetics-600). |
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### Prepare Datasets |
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1. Download HMDB51 video data from [link](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/#Downloads) and extract. Download HMDB51 optical flow data from [link](https://huggingface.co/datasets/hdong51/MultiOOD/blob/main/hmdb51_flow_mp4.tar.gz) and extract. The directory structure should be modified to match: |
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``` |
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HMDB51 |
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├── video |
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| ├── catch |
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| | ├── *.avi |
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| ├── climb |
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| | ├── *.avi |
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| |── ... |
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├── flow |
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| ├── *_flow_x.mp4 |
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| ├── *_flow_y.mp4 |
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| ├── ... |
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``` |
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2. Download UCF101 video data from [link](https://www.crcv.ucf.edu/data/UCF101/UCF101.rar) and extract. Download UCF101 optical flow data from [link](https://huggingface.co/datasets/hdong51/MultiOOD/blob/main/ucf101_flow_mp4.tar.gz) and extract. The directory structure should be modified to match: |
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``` |
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UCF101 |
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├── video |
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| ├── *.avi |
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| |── ... |
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├── flow |
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| ├── *_flow_x.mp4 |
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| ├── *_flow_y.mp4 |
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| ├── ... |
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``` |
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3. Download EPIC-Kitchens video and optical flow data by |
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``` |
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bash utils/download_epic_script.sh |
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``` |
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Download audio data from [link](https://polybox.ethz.ch/index.php/s/PE2zIL99OWXQfMu). |
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Unzip all files and the directory structure should be modified to match: |
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``` |
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EPIC-KITCHENS |
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├── rgb |
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| ├── train |
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| | ├── D3 |
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| | | ├── P22_01.wav |
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| | | ├── P22_01 |
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| | | | ├── frame_0000000000.jpg |
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| | | | ├── ... |
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| | | ├── P22_02 |
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| | | ├── ... |
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| ├── test |
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| | ├── D3 |
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├── flow |
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| ├── train |
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| | ├── D3 |
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| | | ├── P22_01 |
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| | | | ├── frame_0000000000.jpg |
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| | | | ├── ... |
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| | | ├── P22_02 |
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| | | ├── ... |
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| ├── test |
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| | ├── D3 |
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``` |
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4. Download HAC video, audio and optical flow data from [link](https://polybox.ethz.ch/index.php/s/3F8ZWanMMVjKwJK) and extract. The directory structure should be modified to match: |
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``` |
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HAC |
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├── human |
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| ├── videos |
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| | ├── ... |
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| ├── flow |
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| | ├── ... |
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| ├── audio |
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| | ├── ... |
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├── animal |
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| ├── videos |
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| | ├── ... |
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| ├── flow |
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| | ├── ... |
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| ├── audio |
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| | ├── ... |
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├── cartoon |
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| ├── videos |
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| | ├── ... |
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| ├── flow |
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| | ├── ... |
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| ├── audio |
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| | ├── ... |
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``` |
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5. Download Kinetics-600 video data by |
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``` |
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wget -i utils/filtered_k600_train_path.txt |
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``` |
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Extract all files and get audio data from video data by |
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``` |
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python utils/generate_audio_files.py |
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``` |
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Download Kinetics-600 optical flow data (kinetics600_flow_mp4_part_*) from [link](https://huggingface.co/datasets/hdong51/MultiOOD/tree/main) and extract (run `cat kinetics600_flow_mp4_part_* > kinetics600_flow_mp4.tar.gz` and then `tar -zxvf kinetics600_flow_mp4.tar.gz`). |
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Unzip all files and the directory structure should be modified to match: |
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``` |
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Kinetics-600 |
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├── video |
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| ├── acting in play |
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| | ├── *.mp4 |
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| | ├── *.wav |
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| |── ... |
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├── flow |
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| ├── acting in play |
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| | ├── *_flow_x.mp4 |
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| | ├── *_flow_y.mp4 |
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| ├── ... |
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``` |
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### Dataset Splits |
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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. |
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## Methodology |
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<div style="text-align:left"> |
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<img src="frame.jpg" width="70%" height="100%"> |
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</div> |
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--- |
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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. |
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## Contact |
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If you have any questions, please send an email to [email protected] |
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## Citation |
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If you find our work useful in your research please consider citing our paper: |
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``` |
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@article{dong2024multiood, |
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author = {Hao Dong and Yue Zhao and Eleni Chatzi and Olga Fink}, |
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title = {{MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities}}, |
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journal = {arXiv preprint arXiv:2405.17419}, |
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year = {2024}, |
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} |
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``` |
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