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

Recently, there has been substantial progress in image synthesis fromsemantic labelmaps. However, methods used for this task assume the availabilityof complete and unambiguous labelmaps, with instance boundaries of objects, andclass labels for each pixel. This reliance on heavily annotated inputsrestricts the application of image synthesis techniques to real-worldapplications, especially under uncertainty due to weather, occlusion, or noise.On the other hand, algorithms that can synthesize images from sparse labelmapsor sketches are highly desirable as tools that can guide content creators andartists to quickly generate scenes by simply specifying locations of a fewobjects. In this paper, we address the problem of complex scene completion fromsparse labelmaps. Under this setting, very few details about the scene (30\% ofobject instances) are available as input for image synthesis. We propose atwo-stage deep network based method, called `Halluci-Net', that learnsco-occurence relationships between objects in scenes, and then exploits theserelationships to produce a dense and complete labelmap. The generated denselabelmap can then be used as input by state-of-the-art image synthesistechniques like pix2pixHD to obtain the final image. The proposed method isevaluated on the Cityscapes dataset and it outperforms two baselines methods onperformance metrics like Fr\'echet Inception Distance (FID), semanticsegmentation accuracy, and similarity in object co-occurrences. We also showqualitative results on a subset of ADE20K dataset that contains bedroom images.

Halluci-Net: Scene Completion by Exploiting Object Co-occurrence Relationships