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

The cross-depiction problem refers to the task of recognising visual objectsregardless of their depictions; whether photographed, painted, sketched, {\emetc}. In the past, some researchers considered cross-depiction to be domainadaptation (DA). More recent work considers cross-depiction as domaingeneralisation (DG), in which algorithms extend recognition from one set ofdomains (such as photographs and coloured artwork) to another (such assketches). We show that fixing the last layer of AlexNet to random valuesprovides a performance comparable to state of the art DA and DG algorithms,when tested over the PACS benchmark. With support from background literature,our results lead us to conclude that texture alone is insufficient to supportgeneralisation; rather, higher-order representations such as structure andshape are necessary.

Artistic Domain Generalisation Methods are Limited by their Deep Representations