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

Images are more than a collection of objects or attributes -- they representa web of relationships among interconnected objects. Scene Graph has emerged asa new modality for a structured graphical representation of images. Scene Graphencodes objects as nodes connected via pairwise relations as edges. To supportquestion answering on scene graphs, we propose GraphVQA, a language-guidedgraph neural network framework that translates and executes a natural languagequestion as multiple iterations of message passing among graph nodes. Weexplore the design space of GraphVQA framework, and discuss the trade-off ofdifferent design choices. Our experiments on GQA dataset show that GraphVQAoutperforms the state-of-the-art model by a large margin (88.43% vs. 94.78%).

GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering