MJ-Bench/MJ-Bench-Results

--- # For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- ## Citation ``` @misc{chen2024mjbenchmultimodalrewardmodel, title={MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?}, author={Zhaorun Chen and Yichao Du and Zichen Wen and Yiyang Zhou and Chenhang Cui and Zhenzhen Weng and Haoqin Tu and Chaoqi Wang and Zhengwei Tong and Qinglan Huang and Canyu Chen and Qinghao Ye and Zhihong Zhu and Yuqing Zhang and Jiawei Zhou and Zhuokai Zhao and Rafael Rafailov and Chelsea Finn and Huaxiu Yao}, year={2024}, eprint={2407.04842}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.04842}, } ```

原始信息汇总

数据集概述

引用信息

• 标题: MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
• 作者:
  • Zhaorun Chen
  • Yichao Du
  • Zichen Wen
  • Yiyang Zhou
  • Chenhang Cui
  • Zhenzhen Weng
  • Haoqin Tu
  • Chaoqi Wang
  • Zhengwei Tong
  • Qinglan Huang
  • Canyu Chen
  • Qinghao Ye
  • Zhihong Zhu
  • Yuqing Zhang
  • Jiawei Zhou
  • Zhuokai Zhao
  • Rafael Rafailov
  • Chelsea Finn
  • Huaxiu Yao
• 年份: 2024
• arXiv ID: 2407.04842
• 主要类别: cs.CV
• URL: https://arxiv.org/abs/2407.04842

搜集汇总

背景与挑战

背景概述

该数据集是MJ-Bench基准的评估结果，用于衡量多模态奖励模型对文本到图像生成的评判能力，包含多种模型在多个维度（如质量、对齐、偏见、安全）的评分，但数据存在列不一致问题，无法完整加载。