A Step Toward More Inclusive People Annotations for Fairness
This work addresses fairness issues in computer vision by providing more inclusive annotations for researchers, though it is incremental as it builds on an existing dataset.
The authors tackled the problem of incomplete annotations in the Open Images Dataset by creating the MIAP subset with exhaustive bounding boxes and attributes for all visible people, enabling fairness research and analysis of annotation patterns.
The Open Images Dataset contains approximately 9 million images and is a widely accepted dataset for computer vision research. As is common practice for large datasets, the annotations are not exhaustive, with bounding boxes and attribute labels for only a subset of the classes in each image. In this paper, we present a new set of annotations on a subset of the Open Images dataset called the MIAP (More Inclusive Annotations for People) subset, containing bounding boxes and attributes for all of the people visible in those images. The attributes and labeling methodology for the MIAP subset were designed to enable research into model fairness. In addition, we analyze the original annotation methodology for the person class and its subclasses, discussing the resulting patterns in order to inform future annotation efforts. By considering both the original and exhaustive annotation sets, researchers can also now study how systematic patterns in training annotations affect modeling.