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

By assigning each relationship a single label, current approaches formulatethe relationship detection as a classification problem. Under this formulation,predicate categories are treated as completely different classes. However,different from the object labels where different classes have explicitboundaries, predicates usually have overlaps in their semantic meanings. Forexample, sit\_on and stand\_on have common meanings in vertical relationshipsbut different details of how these two objects are vertically placed. In orderto leverage the inherent structures of the predicate categories, we propose tofirst build the language hierarchy and then utilize the Hierarchy GuidedFeature Learning (HGFL) strategy to learn better region features of both thecoarse-grained level and the fine-grained level. Besides, we also propose theHierarchy Guided Module (HGM) to utilize the coarse-grained level to guide thelearning of fine-grained level features. Experiments show that the proposedsimple yet effective method can improve several state-of-the-art baselines by alarge margin (up to $33\%$ relative gain) in terms of Recall@50 on the task ofScene Graph Generation in different datasets.

Exploring the Hierarchy in Relation Labels for Scene Graph Generation