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 increasing tendency to collect large and uncurated datasets to trainvision-and-language models has raised concerns about fair representations. Itis known that even small but manually annotated datasets, such as MSCOCO, areaffected by societal bias. This problem, far from being solved, may be gettingworse with data crawled from the Internet without much control. In addition,the lack of tools to analyze societal bias in big collections of images makesaddressing the problem extremely challenging. Our first contribution is toannotate part of the Google Conceptual Captions dataset, widely used fortraining vision-and-language models, with four demographic and two contextualattributes. Our second contribution is to conduct a comprehensive analysis ofthe annotations, focusing on how different demographic groups are represented.Our last contribution lies in evaluating three prevailing vision-and-languagetasks: image captioning, text-image CLIP embeddings, and text-to-imagegeneration, showing that societal bias is a persistent problem in all of them.

Uncurated Image-Text Datasets: Shedding Light on Demographic Bias