Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender

2023/10/29 by Ahmed Sabir, Sabir, Ahmed, Lluís Padró +1 · 1 citation
Arts and Humanities · Computer Science · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Subtitles and Audiovisual Media #Video Analysis and Summarization

paper · pdf · doi:10.48550/arxiv.2310.19130

openalex publication_date 2023/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• How Bias Binds: Measuring Hidden Associations for Bias Control in Text-to-Image Compositions

Related

• Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models
• Women also Snowboard: Overcoming Bias in Captioning Models
• Mitigating Gender Bias in Captioning Systems
• Analysing Gender Bias in Text-to-Image Models using Object Detection
• Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)