Improve dataset card: Add project/code links, update task category, and expand content (#2)

- Improve dataset card: Add project/code links, update task category, and expand content (d77decad912935ebe9d78b763c46e86ee2e6da48)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,16 +1,45 @@
 ---
-license: apache-2.0
-task_categories:
-- visual-question-answering
 language:
 - en
+license: apache-2.0
 size_categories:
 - 1K<n<10K
+task_categories:
+- image-text-to-text
 tags:
 - vision-language
 - cinematography
 - shotbench
 ---
 
+# ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models
+
 This is the official test set of ShotBench, comprising 3,572 question-answer pairs. Each sample is paired with either an image or a video clip. In total, ShotBench includes 3,049 images and 464 videos, primarily sourced from films that received Oscar nominations for Best Cinematography, ensuring high visual quality and strong cinematic style.
-Please visit our [paper](https://arxiv.org/abs/2506.21356) for more details.
+
+- **Paper:** [ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models](https://arxiv.org/abs/2506.21356)
+- **Project Page:** [https://vchitect.github.io/ShotBench-project/](https://vchitect.github.io/ShotBench-project/)
+- **Code:** [https://github.com/Vchitect/ShotBench](https://github.com/Vchitect/ShotBench)
+
+## Overview
+
+We introduce **ShotBench**, a comprehensive benchmark for evaluating VLMs’ understanding of cinematic language. It comprises over 3.5 k expert-annotated QA pairs derived from images and video clips of over 200 critically acclaimed films (predominantly Oscar-nominated), covering eight distinct cinematography dimensions. This provides a rigorous new standard for assessing fine-grained visual comprehension in film.
+
+We conducted an extensive evaluation of 24 leading VLMs, including prominent open-source and proprietary models, on ShotBench. Our results reveal a critical performance gap: even the most capable model, GPT-4o, achieves less than 60 % average accuracy. This systematically quantifies the current limitations of VLMs in genuine cinematographic comprehension.
+
+To address the identified limitations and facilitate future research, we constructed **ShotQA**, the first large-scale multimodal dataset for cinematography understanding, containing approximately 70 k high-quality QA pairs. Leveraging ShotQA, we developed **ShotVL**, a novel VLM trained using Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO). ShotVL significantly surpasses all tested open-source and proprietary models, establishing a new **state-of-the-art** on ShotBench.
+
+## Citation
+
+If you find ShotBench useful for your research, please cite the following paper:
+
+```bibtex
+@misc{
+      liu2025shotbench,
+      title={ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models}, 
+      author={Hongbo Liu and Jingwen He and Yi Jin and Dian Zheng and Yuhao Dong and Fan Zhang and Ziqi Huang and Yinan He and Yangguang Li and Weichao Chen and Yu Qiao and Wanli Ouyang and Shengjie Zhao and Ziwei Liu},
+      year={2025},
+      eprint={2506.21356},
+      achivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2506.21356}, 
+    }