| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-to-text |
| language: |
| - en |
| --- |
| |
| # Dataset Card for CompreCap |
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| ### 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> |
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| 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). |
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| ### Licensing Information |
| We distribute the annotation under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Use of the images must abide by the Flickr Terms of Use. |
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| ## Citation |
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| 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={CVPR}, |
| year={2025} |
| } |
| ``` |
