Update README.md

README.md

@@ -1,3 +1,41 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+tags:
+- Domain Generalization
+- Multimodal Learning
+pretty_name: Human-Animal-Cartoon
+size_categories:
+- 1K<n<10K
+task_categories:
+- zero-shot-classification
+---
+## Human-Animal-Cartoon dataset
+
+Our Human-Animal-Cartoon (HAC) dataset consists of seven actions (‘sleeping’, ‘watching tv’, ‘eating’, ‘drinking’, ‘swimming’, ‘running’, and ‘opening door’) performed by humans, animals, and cartoon figures, forming three different domains. We collect 3381 video clips from the internet with around 1000 for each domain and provide three modalities in our dataset: video, audio, and pre-computed optical flow.
+
+The dataset can be used for **Multi-modal Domain Generalization and Adaptation**. More details are in [SimMMDG](https://arxiv.org/abs/2310.19795) paper and [code](https://github.com/donghao51/SimMMDG).
+
+## Related Projects
+
+[SimMMDG](https://github.com/donghao51/SimMMDG): A Simple and Effective Framework for Multi-modal Domain Generalization
+
+[MOOSA](https://github.com/donghao51/MOOSA): Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision
+
+[MultiOOD](https://github.com/donghao51/MultiOOD): Scaling Out-of-Distribution Detection for Multiple Modalities
+
+## Citation
+
+If you find our dataset useful in your research please consider citing our paper:
+
+```
+@inproceedings{dong2023SimMMDG,
+    title={Sim{MMDG}: A Simple and Effective Framework for Multi-modal Domain Generalization},
+    author={Dong, Hao and Nejjar, Ismail and Sun, Han and Chatzi, Eleni and Fink, Olga},
+    booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+    year={2023}
+}
+```
+
+<img src="hac.jpg"  width="50%" height="50%">