Add pipeline tag and links to paper/code

Hi! I'm Niels, part of the community science team at Hugging Face.

I noticed that this model card could benefit from some additional metadata and links to the research it belongs to. This PR updates the README to include:
- The `video-classification` pipeline tag in the metadata for better discoverability.
- Links to the [official paper](https://huggingface.co/papers/2603.12254), project page, and GitHub repository.
- A BibTeX citation section.

These changes help researchers and users find and cite your work more easily!

README.md

@@ -2,15 +2,20 @@
 license: other
 license_name: nvidia
 license_link: LICENSE
+pipeline_tag: video-classification
 ---
 
+# VideoMAE_AutoGaze
 
+[**Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing**](https://huggingface.co/papers/2603.12254)
+
+[**Project Page**](https://autogaze.github.io/) | [**GitHub**](https://github.com/NVlabs/AutoGaze) | [**Demo**](https://huggingface.co/spaces/bfshi/AutoGaze)
 
 ## Model Overview
 
 ### Description:
 
-VideoMAE model used for training AutoGaze. This model is for research and development only.  <br>
+VideoMAE model used for training AutoGaze. This model is for research and development only. <br>
 
 ### License/Terms of Use:
 
@@ -124,3 +129,17 @@ The raw videos are collected from public dataset including Ego4D, 100DoH, Intern
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). <br> 
 
 Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included. <br>
+
+## Citation
+
+```bibtex
+@misc{shi2026attendattentionefficientscalable,
+      title={Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing}, 
+      author={Baifeng Shi and Stephanie Fu and Long Lian and Hanrong Ye and David Eigen and Aaron Reite and Boyi Li and Jan Kautz and Song Han and David M. Chan and Pavlo Molchanov and Trevor Darrell and Hongxu Yin},
+      year={2026},
+      eprint={2603.12254},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2603.12254}, 
+}
+```