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--- |
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license: cc-by-nc-4.0 |
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base_model: |
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- mistralai/Mistral-7B-Instruct-v0.3 |
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tags: |
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- video |
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- audio |
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- multimodal |
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--- |
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# [Vidi: Large Multimodal Models for Video Understanding and Editing](https://arxiv.org/pdf/2504.15681) |
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Homepage: [https://bytedance.github.io/vidi-website/](https://bytedance.github.io/vidi-website/) |
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Github: [https://github.com/bytedance/vidi](https://github.com/bytedance/vidi) |
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Demo: [https://vidi.byteintl.com/](https://vidi.byteintl.com/) |
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> 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. |
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This model is the first release for temporal retrieval. |
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Please find the inference and evaluation code on [https://github.com/bytedance/vidi](https://github.com/bytedance/vidi). |
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## Citation |
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If you find Vidi useful for your research and applications, please cite using this BibTeX: |
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``` |
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@article{Vidi2025vidi2, |
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title={Vidi2: Large Multimodal Models for Video |
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Understanding and Creation}, |
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author={Vidi Team, Celong Liu, Chia-Wen Kuo, Chuang Huang, |
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Dawei Du, Fan Chen, Guang Chen, Haoji Zhang, |
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Haojun Zhao, Lingxi Zhang, Lu Guo, Lusha Li, |
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Longyin Wen, Qihang Fan, Qingyu Chen, Rachel Deng, |
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Sijie Zhu, Stuart Siew, Tong Jin, Weiyan Tao, |
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Wen Zhong, Xiaohui Shen, Xin Gu, Zhenfang Chen, Zuhua Lin}, |
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journal={arXiv preprint arXiv:2511.19529}, |
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year={2025} |
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} |
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@article{Vidi2025vidi, |
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title={Vidi: Large Multimodal Models for Video |
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Understanding and Editing}, |
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author={Vidi Team, Celong Liu, Chia-Wen Kuo, Dawei Du, |
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Fan Chen, Guang Chen, Jiamin Yuan, Lingxi Zhang, |
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Lu Guo, Lusha Li, Longyin Wen, Qingyu Chen, |
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Rachel Deng, Sijie Zhu, Stuart Siew, Tong Jin, |
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Wei Lu, Wen Zhong, Xiaohui Shen, Xin Gu, Xing Mei, |
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Xueqiong Qu, Zhenfang Chen}, |
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journal={arXiv preprint arXiv:2504.15681}, |
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year={2025} |
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} |
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``` |
