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.

中文翻译：

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迈向更具包容性的人们诠释公平的一步

开放图像数据集包含大约900万张图像，是计算机视觉研究的广泛接受的数据集。按照大型数据集的常规做法，注释并不是穷尽的，仅在每个图像的类的子集上有边框和属性标签。在本文中，我们在开放图像数据集的一个子集MIAP（人的更多包容性注释）子集中展示了一组新的注释，其中包含这些图像中所有可见人的边界框和属性。MIAP子集的属性和标记方法旨在进行模型公平性的研究。此外，我们分析了人员类及其子类的原始注释方法，并讨论了生成的模式，以便为将来的注释工作提供信息。