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

We address the task of annotating images with semantic tuples. Solving thisproblem requires an algorithm which is able to deal with hundreds of classesfor each argument of the tuple. In such contexts, data sparsity becomes a keychallenge, as there will be a large number of classes for which only a fewexamples are available. We propose handling this by incorporating featurerepresentations of both the inputs (images) and outputs (argument classes) intoa factorized log-linear model, and exploiting the flexibility of scoringfunctions based on bilinear forms. Experiments show that integrating featurerepresentations of the outputs in the structured prediction model leads tobetter overall predictions. We also conclude that the best outputrepresentation is specific for each type of argument.

Structured Prediction with Output Embeddings for Semantic Image Annotation