  Language is increasingly being used to define rich visual recognition
problems with supporting image collections sourced from the web. Structured
prediction models are used in these tasks to take advantage of correlations
between co-occurring labels and visual input but risk inadvertently encoding
social biases found in web corpora. In this work, we study data and models
associated with multilabel object classification and visual semantic role
labeling. We find that (a) datasets for these tasks contain significant gender
bias and (b) models trained on these datasets further amplify existing bias.
For example, the activity cooking is over 33% more likely to involve females
than males in a training set, and a trained model further amplifies the
disparity to 68% at test time. 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 and visual semantic role labeling, respectively.
