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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Assessing the artness of AI-generated images continues to be a challengewithin the realm of image generation. Most existing metrics cannot be used toperform instance-level and reference-free artness evaluation. This paperpresents ArtScore, a metric designed to evaluate the degree to which an imageresembles authentic artworks by artists (or conversely photographs), therebyoffering a novel approach to artness assessment. We first blend pre-trainedmodels for photo and artwork generation, resulting in a series of mixed models.Subsequently, we utilize these mixed models to generate images exhibitingvarying degrees of artness with pseudo-annotations. Each photorealistic imagehas a corresponding artistic counterpart and a series of interpolated imagesthat range from realistic to artistic. This dataset is then employed to train aneural network that learns to estimate quantized artness levels of arbitraryimages. Extensive experiments reveal that the artness levels predicted byArtScore align more closely with human artistic evaluation than existingevaluation metrics, such as Gram loss and ArtFID.

Learning to Evaluate the Artness of AI-generated Images