Add pipeline tag, license, and links to paper/code

README.md

@@ -1,14 +1,33 @@
+---
+license: cc-by-nc-4.0
+pipeline_tag: video-text-to-text
+---
+
 # ST-Evidence-7B
 
-We propose **Evidence-Backed Video Question Answering** (E-VQA), a task where multimodal models are designed to jointly produce a semantic **textual answer** and associated spatiotemporal evidence. This evidence includes **temporal segments** and **dense, tracked object segmentation masklets**. A masklet is defined as a temporal sequence of object segmentation masks tracked over time.
+This repository contains the official **ST-Evidence-7B** model introduced in the paper [Evidence-Backed Video Question Answering](https://huggingface.co/papers/2607.11862).
 
-Our model was fine-tuned from UniPixel, which is built upon QWen2.5-VL and SAM 2.1. UniPixel is a unified model designed to handle both video question answering and mask generation.
+* **Code**: [GitHub - SalesforceAIResearch/EVQA](https://github.com/SalesforceAIResearch/EVQA)
+* **Benchmark (ST-Evidence)**: [Salesforce/ST-Evidence-Bench](https://huggingface.co/datasets/Salesforce/ST-Evidence-Bench)
+* **SFT Dataset (ST-Evidence-Instruct)**: [Salesforce/ST-Evidence-Instruct](https://huggingface.co/datasets/Salesforce/ST-Evidence-Instruct)
 
-This was released for research purposes only, in support of the academic paper *Evidence-Backed Video Question Answering*.
+## Model Description
+
+We propose **Evidence-Backed Video Question Answering** (E-VQA), a task where multimodal models are designed to jointly produce a semantic **textual answer** and associated spatiotemporal evidence. This evidence includes **temporal segments** and **dense, tracked object segmentation masklets**. A masklet is defined as a temporal sequence of object segmentation masks tracked over time.
 
+Our model was fine-tuned from UniPixel, which is built upon Qwen2.5-VL and SAM 2.1. UniPixel is a unified model designed to handle both video question answering and mask generation.
 
-## License
+This model was released for research purposes only, in support of the academic paper *Evidence-Backed Video Question Answering*.
 
-CC-BY-NC 4.0
+## Citation
 
+If you find this work useful for your research, please cite our paper:
 
+```bibtex
+@inproceedings{wang2026evidence,
+  title={Evidence-Backed Video Question Answering},
+  author={Wang, Shijie and Zhou, Honglu and Wang, Ziyang and Xu, Ran and Xiong, Caiming and Savarese, Silvio and Sun, Chen and Niebles, Juan Carlos},
+  booktitle={European Conference on Computer Vision (ECCV)},
+  year={2026}
+}
+```