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 propose a new task towards more practical application for image generation- high-quality image synthesis from salient object layout. This new settingallows users to provide the layout of salient objects only (i.e., foregroundbounding boxes and categories), and lets the model complete the drawing with aninvented background and a matching foreground. Two main challenges spring fromthis new task: (i) how to generate fine-grained details and realistic textureswithout segmentation map input; and (ii) how to create a background and weaveit seamlessly into standalone objects. To tackle this, we propose BackgroundHallucination Generative Adversarial Network (BachGAN), which first selects aset of segmentation maps from a large candidate pool via a background retrievalmodule, then encodes these candidate layouts via a background fusion module tohallucinate a suitable background for the given objects. By generating thehallucinated background representation dynamically, our model can synthesizehigh-resolution images with both photo-realistic foreground and integralbackground. Experiments on Cityscapes and ADE20K datasets demonstrate theadvantage of BachGAN over existing methods, measured on both visual fidelity ofgenerated images and visual alignment between output images and input layouts.

BachGAN: High-Resolution Image Synthesis from Salient Object Layout