Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
Submission Type: Regular Short Paper
Submission Track: Language Grounding to Vision, Robotics and Beyond
Submission Track 2: NLP Applications
Keywords: image captioning, visual grounding, gender bias
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.
Submission Number: 3988
Loading