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

We propose a method for visual question answering which combines an internalrepresentation of the content of an image with information extracted from ageneral knowledge base to answer a broad range of image-based questions. Thisallows more complex questions to be answered using the predominant neuralnetwork-based approach than has previously been possible. It particularlyallows questions to be asked about the contents of an image, even when theimage itself does not contain the whole answer. The method constructs a textualrepresentation of the semantic content of an image, and merges it with textualinformation sourced from a knowledge base, to develop a deeper understanding ofthe scene viewed. Priming a recurrent neural network with this combinedinformation, and the submitted question, leads to a very flexible visualquestion answering approach. We are specifically able to answer questions posedin natural language, that refer to information not contained in the image. Wedemonstrate the effectiveness of our model on two publicly available datasets,Toronto COCO-QA and MS COCO-VQA and show that it produces the best reportedresults in both cases.

Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources