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--- |
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license: cc-by-4.0 |
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task_categories: |
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- image-to-text |
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language: |
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- en |
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--- |
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# Dataset Card for CompreCap |
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### Dataset Description |
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The CompreCap benchmark is characterized by human-annotated scene graph and focuses on the evaluation of comprehensive image captioning. |
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It provides new semantic segmentation annotations for common objects in images, with an average mask coverage of 95.83%. |
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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: |
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<div align="center"> |
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<img src="graph_anno.png" alt="CompreCap" width="1200" height="auto"> |
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</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). |
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Based on the CompreCap benchmark, researchers can comprehensively accessing the quality of image captions generated by large vision-language models. |
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The evaluation code is available [here](https://github.com/LuFan31/CompreCap). |
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### Licensing Information |
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We distribute the image under a standard Creative Common CC-BY-4.0 license. The individual images are under their own copyrights. |
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## Citation |
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BibTeX: |
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```bibtex |
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@article{CompreCap, |
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title={Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning}, |
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author={Fan Lu, Wei Wu, Kecheng Zheng, Shuailei Ma, Biao Gong, Jiawei Liu, Wei Zhai, Yang Cao, Yujun Shen, Zheng-Jun Zha}, |
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booktitle={arXiv}, |
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year={2024} |
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} |
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``` |
