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 accuracy of state-of-the-art Faster R-CNN and YOLO object detectors areevaluated and compared on a special masked MS COCO dataset to measure how muchtheir predictions rely on contextual information encoded at object categorylevel. Category level representation of context is motivated by the fact thatit could be an adequate way to transfer knowledge between visual and non-visualdomains. According to our measurements, current detectors usually do not buildstrong dependency on contextual information at category level, however, whenthey does, they does it in a similar way, suggesting that contextual dependenceof object categories is an independent property that is relevant to betransferred.

Analysing object detectors from the perspective of co-occurring object categories