Update README.md

README.md

@@ -5,13 +5,13 @@ license: mit
 
 
 
-<a href='https://arxiv.org/abs/'><img src='https://img.shields.io/badge/arXiv-paper-red'></a><a href='https://ruili33.github.io/tpo_website/'><img src='https://img.shields.io/badge/project-TPO-blue'></a>
+<a href='https://arxiv.org/abs/2501.13919v1'><img src='https://img.shields.io/badge/arXiv-paper-red'></a><a href='https://ruili33.github.io/tpo_website/'><img src='https://img.shields.io/badge/project-TPO-blue'></a>
 <a href='https://huggingface.co/collections/ruili0/temporal-preference-optimization-67874b451f65db189fa35e10'><img src='https://img.shields.io/badge/model-checkpoints-yellow'></a> 
 <a href='https://github.com/ruili33/TPO'><img src='https://img.shields.io/badge/github-repository-purple'></a> 
 <img src="cvpr_figure_TPO.png"></img>
 # LLaVA-Video-7B-Qwen2-TPO
 
-LLaVA-Video-7B-Qwen2-TPO, introduced by paper [Temporal Preference Optimization for Long-form Video Understanding](https://arxiv.org/abs), optimized
+LLaVA-Video-7B-Qwen2-TPO, introduced by paper [Temporal Preference Optimization for Long-form Video Understanding](https://arxiv.org/abs/2501.13919v1), optimized
 by temporal preference based on LLaVA-Video-7B-Qwen2. The LLaVA-Video-7B-Qwen2-TPO model establishes state-of-the-art performance across a range of 
 benchmarks, demonstrating an average performance improvement of 1.5% compared to LLaVA-Video-7B. 
 Notably, it emerges as the leading 7B parameter model on the Video-MME benchmark.
@@ -99,13 +99,23 @@ print(text_outputs)
 This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the OpenAI Terms of Use for the dataset and the specific licenses for base language models (Qwen2 license). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.
 
 
-## Citation [optional]
+## Citation 
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
+```
+@misc{li2025temporalpreferenceoptimizationlongform,
+      title={Temporal Preference Optimization for Long-Form Video Understanding}, 
+      author={Rui Li and Xiaohan Wang and Yuhui Zhang and Zeyu Wang and Serena Yeung-Levy},
+      year={2025},
+      eprint={2501.13919},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2501.13919}, 
+}
+```
 
 **References:**