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

Detecting visual relationships between people and clothing in an image hasbeen a relatively unexplored problem in the field of computer vision andbiometrics. The lack readily available public dataset for ``worn'' and``unworn'' classification has slowed the development of solutions for thisproblem. We present the release of the Relatable Clothing Dataset whichcontains 35287 person-clothing pairs and segmentation masks for the developmentof ``worn'' and ``unworn'' classification models. Additionally, we propose anovel soft attention unit for performing ``worn'' and ``unworn'' classificationusing deep neural networks. The proposed soft attention models have an accuracyof upward $98.55\% \pm 0.35\%$ on the Relatable Clothing Dataset anddemonstrate high generalizable, allowing us to classify unseen articles ofclothing such as high visibility vests as ``worn'' or ``unworn''.

Relatable Clothing: Detecting Visual Relationships between People and Clothing