---
license: cc-by-4.0
task_categories:
- image-to-text
language:
- en
---

# Dataset Card for CompreCap


### Dataset Description
The CompreCap benchmark is characterized by human-annotated scene graph and focuses on the evaluation of comprehensive image captioning.
It provides new semantic segmentation annotations for common objects in images, with an average mask coverage of 95.83%.
Beyond the careful annotation of objects, CompreCap also includes high-quality descriptions of the attributes bound to the objects, as well as directional relation descriptions between the objects, composing a complete and directed scene graph structure:
<div align="center">
  <img src="graph_anno.png" alt="CompreCap" width="1200" height="auto">
</div>

The annotations of segmentation masks, category names, the descriptions of attributes and relationships are saved in [./anno.json](https://huggingface.co/datasets/FanLu31/CompreCap/blob/main/anno.json). 
Based on the CompreCap benchmark, researchers can comprehensively accessing the quality of image captions generated by large vision-language models.
The evaluation code is available [here](https://github.com/LuFan31/CompreCap).

### Licensing Information
We distribute the image under a standard Creative Common CC-BY-4.0 license. The individual images are under their own copyrights.


## Citation


BibTeX:
```bibtex
@article{CompreCap,
  title={Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning},
  author={Fan Lu, Wei Wu, Kecheng Zheng, Shuailei Ma, Biao Gong, Jiawei Liu, Wei Zhai, Yang Cao, Yujun Shen, Zheng-Jun Zha},
  booktitle={arXiv},
  year={2024}
}
```