Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
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Abstract (arXiv)
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 urlhttps://github.com/ahmedssabir/GenderScore.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 29 Oct 2023.
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15 more
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