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

Obtaining demographics information from video is valuable for a range ofreal-world applications. While approaches that leverage facial features forgender inference are very successful in restrained environments, they do notwork in most real-world scenarios when the subject is not facing the camera,has the face obstructed or the face is not clear due to distance from thecamera or poor resolution. We propose a weakly-supervised method for learninggender information of people based on their manner of walking. We make use ofstate-of-the art facial analysis models to automatically annotate front-viewwalking sequences and generalise to unseen angles by leveraging gait-basedlabel propagation. Our results show on par or higher performance with facialanalysis models with an F1 score of 91% and the ability to successfullygeneralise to scenarios in which facial analysis is unfeasible due to subjectsnot facing the camera or having the face obstructed.

From Face to Gait: Weakly-Supervised Learning of Gender Information from Walking Patterns