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

Image captioning as a multimodal task has drawn much interest in recentyears. However, evaluation for this task remains a challenging problem.Existing evaluation metrics focus on surface similarity between a candidatecaption and a set of reference captions, and do not check the actual relationbetween a caption and the underlying visual content. We introduce a newdiagnostic evaluation framework for the task of image captioning, with the goalof directly assessing models for grammaticality, truthfulness and diversity(GTD) of generated captions. We demonstrate the potential of our evaluationframework by evaluating existing image captioning models on a wide ranging setof synthetic datasets that we construct for diagnostic evaluation. Weempirically show how the GTD evaluation framework, in combination withdiagnostic datasets, can provide insights into model capabilities andlimitations to supplement standard evaluations.

Going Beneath the Surface: Evaluating Image Captioning for Grammaticality, Truthfulness and Diversity