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

This paper presents a new baseline for visual question answering task. Givenan image and a question in natural language, our model produces accurateanswers according to the content of the image. Our model, while beingarchitecturally simple and relatively small in terms of trainable parameters,sets a new state of the art on both unbalanced and balanced VQA benchmark. OnVQA 1.0 open ended challenge, our model achieves 64.6% accuracy on thetest-standard set without using additional data, an improvement of 0.4% overstate of the art, and on newly released VQA 2.0, our model scores 59.7% onvalidation set outperforming best previously reported results by 0.5%. Theresults presented in this paper are especially interesting because very similarmodels have been tried before but significantly lower performance werereported. In light of the new results we hope to see more meaningful researchon visual question answering in the future.

Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering