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/

Zendy Plus

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

Zendy Tools

Zendy Open

In this work, we propose a novel topic consisting of two dual tasks: 1) givena scene, recommend objects to insert, 2) given an object category, retrievesuitable background scenes. A bounding box for the inserted object is predictedin both tasks, which helps downstream applications such as semi-automatedadvertising and video composition. The major challenge lies in the fact thatthe target object is neither present nor localized at test time, whereasavailable datasets only provide scenes with existing objects. To tackle thisproblem, we build an unsupervised algorithm based on object-level contexts,which explicitly models the joint probability distribution of object categoriesand bounding boxes with a Gaussian mixture model. Experiments on our newlyannotated test set demonstrate that our system outperforms existing baselineson all subtasks, and do so under a unified framework. Our contribution promisesfuture extensions and applications.

What and Where: A Context-based Recommendation System for Object Insertion