| | --- |
| | 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> |
| |
|
| | 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 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} |
| | } |
| | ``` |
