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

Current face recognition systems typically operate via classification intoknown identities obtained from supervised identity annotations. There are twoproblems with this paradigm: (1) current systems are unable to benefit fromoften abundant unlabelled data; and (2) they equate successful recognition withlabelling a given input image. Humans, on the other hand, regularly performidentification of individuals completely unsupervised, recognising the identityof someone they have seen before even without being able to name thatindividual. How can we go beyond the current classification paradigm towards amore human understanding of identities? In previous work, we proposed anintegrated Bayesian model that coherently reasons about the observed images,identities, partial knowledge about names, and the situational context of eachobservation. Here, we propose extensions of the contextual component of thismodel, enabling unsupervised discovery of an unbounded number of contexts forimproved face recognition.

Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model