Update README.md

README.md

@@ -1,3 +1,207 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- feature-extraction
+language:
+- en
+tags:
+- Out-of-Distribution Detection
+- Multimodal Learning
+pretty_name: MultiOOD
+size_categories:
+- 100K<n<1M
+---
+<div align="center">
+
+<h1>MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities</h1>
+
+<div>
+    <a href='https://sites.google.com/view/dong-hao/' target='_blank'>Hao Dong</a><sup>1</sup>
+    &emsp;
+    <a href='https://viterbi-web.usc.edu/~yzhao010/' target='_blank'>Yue Zhao</a><sup>2</sup>
+    &emsp;
+    <a href='https://chatzi.ibk.ethz.ch/about-us/people/prof-dr-eleni-chatzi.html' target='_blank'>Eleni Chatzi</a><sup>1</sup>
+    &emsp;
+    <a href='https://people.epfl.ch/olga.fink?lang=en' target='_blank'>Olga Fink</a><sup>3</sup>
+</div>
+<div>
+    <sup>1</sup>ETH Zurich, <sup>2</sup>University of Southern California, <sup>3</sup>EPFL
+</div>
+
+
+<div>
+    <h4 align="center">
+        • <a href="https://arxiv.org/abs/2405." target='_blank'>arXiv</a> •
+    </h4>
+</div>
+
+
+
+<div style="text-align:center">
+<img src="multiood.jpg"  width="100%" height="100%">
+</div>
+
+---
+
+</div>
+
+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
+1. Download HMDB51 video data from [link](http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/hmdb51_org.rar) 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:
+
+```
+HMDB51
+├── video
+|   ├── catch
+|   |   ├── *.avi
+|   ├── climb
+|   |   ├── *.avi
+|   |── ...
+
+
+├── flow
+|   ├── *_flow_x.mp4
+|   ├── *_flow_y.mp4
+|   ├── ...
+```
+
+1. 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:
+
+```
+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](https://polybox.ethz.ch/index.php/s/PE2zIL99OWXQfMu).
+
+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](https://polybox.ethz.ch/index.php/s/3F8ZWanMMVjKwJK) 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](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`).
+
+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
+<div style="text-align:left">
+<img src="frame.jpg"  width="70%" height="100%">
+</div>
+
+---
+
+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
+https://github.com/donghao51/MultiOOD
+
+## 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}},
+	year     = {2024},
+}
+```