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

Learning a joint language-visual embedding has a number of very appealingproperties and can result in variety of practical application, includingnatural language image/video annotation and search. In this work, we studythree different joint language-visual neural network model architectures. Weevaluate our models on large scale LSMDC16 movie dataset for two tasks: 1)Standard Ranking for video annotation and retrieval 2) Our proposed moviemultiple-choice test. This test facilitate automatic evaluation ofvisual-language models for natural language video annotation based on humanactivities. In addition to original Audio Description (AD) captions, providedas part of LSMDC16, we collected and will make available a) manually generatedre-phrasings of those captions obtained using Amazon MTurk b) automaticallygenerated human activity elements in "Predicate + Object" (PO) phrases based on"Knowlywood", an activity knowledge mining model. Our best model archivesRecall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subsetof 1000 samples. For multiple-choice test, our best model achieve accuracy58.11% over whole LSMDC16 public test-set.

Learning Language-Visual Embedding for Movie Understanding with Natural-Language