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

@inproceedings{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},
  booktitle={EMNLP},
  year={2017}
}
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…
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