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

Visual Question Answering (VQA) has benefited from increasingly sophisticatedmodels, but has not enjoyed the same level of engagement in terms of datacreation. In this paper, we propose a method that automatically derives VQAexamples at volume, by leveraging the abundance of existing image-captionannotations combined with neural models for textual question generation. Weshow that the resulting data is of high-quality. VQA models trained on our dataimprove state-of-the-art zero-shot accuracy by double digits and achieve alevel of robustness that lacks in the same model trained on human-annotated VQAdata.

All You May Need for VQA are Image Captions