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.

此论文介绍了Open Images数据集下的MIAP子集，包含了所有可见人物的边界框和属性标签，并讨论了MIAP子集的属性和标注方法对模型公平性研究的贡献和原始注释方法及其子类的分析结果，为未来注释工作提供参考，帮助研究人员了解训练注释中的系统模式如何影响建模

迈向更加包容的人注释以实现公平性