Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

@article{Zhao2017MenAL,
  title={Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints},
  author={Jieyu Zhao and Tianlu Wang and Mark Yatskar and Vicente Ordonez and Kai-Wei Chang},
  journal={ArXiv},
  year={2017},
  volume={abs/1707.09457}
}
Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. [...] Key Method We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification…Expand Abstract

Citations

Publications citing this paper.
SHOWING 1-10 OF 168 CITATIONS

Testing Deep Neural Network based Image Classifiers

VIEW 10 EXCERPTS
CITES METHODS & BACKGROUND

Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation

VIEW 11 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Addressing and Understanding Shortcomings in Vision and Language

VIEW 10 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function

VIEW 6 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Adversarial Removal of Gender from Deep Image Representations

VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS

FILTER CITATIONS BY YEAR

2017
2020

CITATION STATISTICS

  • 16 Highly Influenced Citations

  • Averaged 53 Citations per year from 2017 through 2019

  • 85% Increase in citations per year in 2019 over 2018

References

Publications referenced by this paper.
SHOWING 1-10 OF 32 REFERENCES

Microsoft COCO Captions: Data Collection and Evaluation Server

VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

Situation Recognition: Visual Semantic Role Labeling for Image Understanding

VIEW 6 EXCERPTS

Combinatorial optimization: theory, computation, and applications

VIEW 1 EXCERPT

Commonly Uncommon: Semantic Sparsity in Situation Recognition

VIEW 1 EXCERPT

Equality of Opportunity in Supervised Learning

VIEW 1 EXCERPT