Datasets:
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:
The annotations of segmentation masks, category names, the descriptions of attributes and relationships are saved in ./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.
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.
Citation
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}
}