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：在本文中，我们研究了物体对图像描述系统中性别偏见的影响。我们的结果表明，只有性别特定物体（如女性-口红）才具有强烈的性别偏见。此外，我们提出了一种基于视觉语义的性别得分，该得分可衡量偏见程度，并可作为插件应用于任何图像描述系统。我们的实验验证了该性别得分的实用性，因为我们观察到该得分能够衡量描述与其关联性别之间的偏见关系；因此，该得分可作为现有Object Gender Co-Occ方法的补充度量指标。代码和数据已公开于\url{https://github.com/ahmedssabir/GenderScore}。