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

How does one represent an action? How does one describe an action that wehave never seen before? Such questions are addressed by the Zero Shot Learningparadigm, where a model is trained on only a subset of classes and is evaluatedon its ability to correctly classify an example from a class it has never seenbefore. In this work, we present a body pose based zero shot action recognitionnetwork and demonstrate its performance on the NTU RGB-D dataset. Our modellearns to jointly encapsulate visual similarities based on pose features of theaction performer as well as similarities in the natural language descriptionsof the unseen action class names. We demonstrate how this pose-languagesemantic space encodes knowledge which allows our model to correctly predictactions not seen during training.

Skeleton based Zero Shot Action Recognition in Joint Pose-Language Semantic Space