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

Providing systems the ability to relate linguistic and visual content is oneof the hallmarks of computer vision. Tasks such as text-based image retrievaland image captioning were designed to test this ability but come withevaluation measures that have a high variance or are difficult to interpret. Westudy an alternative task for systems that match text and images: given a textquery, the system is asked to select the image that best matches the query froma pair of semantically similar images. The system's accuracy on this BinaryImage SelectiON (BISON) task is interpretable, eliminates the reliabilityproblems of retrieval evaluations, and focuses on the system's ability tounderstand fine-grained visual structure. We gather a BISON dataset thatcomplements the COCO dataset and use it to evaluate modern text-based imageretrieval and image captioning systems. Our results provide novel insights intothe performance of these systems. The COCO-BISON dataset and correspondingevaluation code are publicly available from \url{http://hexianghu.com/bison/}.

Evaluating Text-to-Image Matching using Binary Image Selection (BISON)