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

Automatically describing the content of an image is a fundamental problem inartificial intelligence that connects computer vision and natural languageprocessing. In this paper, we present a generative model based on a deeprecurrent architecture that combines recent advances in computer vision andmachine translation and that can be used to generate natural sentencesdescribing an image. The model is trained to maximize the likelihood of thetarget description sentence given the training image. Experiments on severaldatasets show the accuracy of the model and the fluency of the language itlearns solely from image descriptions. Our model is often quite accurate, whichwe verify both qualitatively and quantitatively. For instance, while thecurrent state-of-the-art BLEU-1 score (the higher the better) on the Pascaldataset is 25, our approach yields 59, to be compared to human performancearound 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66,and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, weachieve a BLEU-4 of 27.7, which is the current state-of-the-art.

Show and Tell: A Neural Image Caption Generator