Update README.md

README.md

@@ -6,4 +6,35 @@ tags:
 - video
 - audio
 - multimodal
----
+---
+
+# [Vidi: Large Multimodal Models for Video Understanding and Editing](https://arxiv.org/pdf/2504.15681)
+
+Homepage: [https://bytedance.github.io/vidi-website/](https://bytedance.github.io/vidi-website/)
+
+Github: [https://github.com/bytedance/vidi](https://github.com/bytedance/vidi)
+
+Demo: [https://vidi.byteintl.com/](https://vidi.byteintl.com/)
+
+> We introduce Vidi, a family of Large Multimodal Models (LMMs) for a wide range of video understanding and editing (VUE) scenarios. The first release focuses on temporal retrieval (TR), i.e., identifying the time ranges in input videos corresponding to a given text query.
+
+This model is the first release for temporal retrieval. 
+
+Please find the inference and evaluation code on [https://github.com/bytedance/vidi](https://github.com/bytedance/vidi).
+
+## Citation
+If you find Vidi useful for your research and applications, please cite using this BibTeX:
+```
+@article{Vidi2025vidi,
+    title={Vidi: Large Multimodal Models for Video 
+            Understanding and Editing},
+    author={Vidi Team, Celong Liu, Chia-Wen Kuo, Dawei Du, 
+            Fan Chen, Guang Chen, Jiamin Yuan, Lingxi Zhang,
+            Lu Guo, Lusha Li, Longyin Wen, Qingyu Chen, 
+            Rachel Deng, Sijie Zhu, Stuart Siew, Tong Jin, 
+            Wei Lu, Wen Zhong, Xiaohui Shen, Xin Gu, Xing Mei, 
+            Xueqiong Qu},
+    journal={arXiv preprint arXiv:2504.15681},
+    year={2025}
+}
+```