English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The ImageNet dataset ushered in a flood of academic and industry interest indeep learning for computer vision applications. Despite its significant impact,there has not been a comprehensive investigation into the demographicattributes of images contained within the dataset. Such a study could lead tonew insights on inherent biases within ImageNet, particularly important givenit is frequently used to pretrain models for a wide variety of computer visiontasks. In this work, we introduce a model-driven framework for the automaticannotation of apparent age and gender attributes in large-scale image datasets.Using this framework, we conduct the first demographic audit of the 2012ImageNet Large Scale Visual Recognition Challenge (ILSVRC) subset of ImageNetand the "person" hierarchical category of ImageNet. We find that 41.62% offaces in ILSVRC appear as female, 1.71% appear as individuals above the age of60, and males aged 15 to 29 account for the largest subgroup with 27.11%. Wenote that the presented model-driven framework is not fair for allintersectional groups, so annotation are subject to bias. We present this workas the starting point for future development of unbiased annotation models andfor the study of downstream effects of imbalances in the demographics ofImageNet. Code and annotations are available at:http://bit.ly/ImageNetDemoAudit

Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets