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/

Zendy Plus

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

Zendy Tools

Zendy Open

We present an approach to searching large video corpora for video clips whichdepict a natural-language query in the form of a sentence. This approach usescompositional semantics to encode subtle meaning that is lost in other systems,such as the difference between two sentences which have identical words butentirely different meaning: "The person rode the horse} vs. \emph{The horserode the person". Given a video-sentence pair and a natural-language parser,along with a grammar that describes the space of sentential queries, we producea score which indicates how well the video depicts the sentence. We producesuch a score for each video clip in a corpus and return a ranked list of clips.Furthermore, this approach addresses two fundamental problems simultaneously:detecting and tracking objects, and recognizing whether those tracks depict thequery. Because both tracking and object detection are unreliable, this usesknowledge about the intended sentential query to focus the tracker on therelevant participants and ensures that the resulting tracks are described bythe sentential query. While earlier work was limited to single-word querieswhich correspond to either verbs or nouns, we show how one can search forcomplex queries which contain multiple phrases, such as prepositional phrases,and modifiers, such as adverbs. We demonstrate this approach by searching for141 queries involving people and horses interacting with each other in 10full-length Hollywood movies.

Saying What You're Looking For: Linguistics Meets Video Search