In this paper, we investigate the impact of objects on gender bias in image captioning systems. Our results show that only gender-specific objects have a strong gender bias (e.g., women-lipstick). In addition, we propose a visual semantic-based gender score that measures the degree of bias and can be used as a plug-in for any image captioning system. Our experiments demonstrate the utility of the gender score, since we observe that our score can measure the bias relation between a caption and its related gender; therefore, our score can be used as an additional metric to the existing Object Gender Co-Occ approach. Code and data are publicly available at \url{https://github.com/ahmedssabir/GenderScore}.

翻译：本文探究了物体对图像描述系统中性别偏见的影响。研究结果表明，仅性别特异性物体（如女性-口红）表现出显著的性别偏见。此外，我们提出了一种基于视觉语义的性别评分方法，该评分可量化偏见程度，并可作为插件集成至任意图像描述系统中。实验验证了该性别评分的实用性，我们观察到该评分能够衡量描述文本与其关联性别之间的偏见关系。因此，该评分可作为现有对象性别共现方法的补充评估指标。相关代码与数据公开于\url{https://github.com/ahmedssabir/GenderScore}。