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

Image harmonization has been significantly advanced with large-scaleharmonization dataset. However, the current way to build dataset is stilllabor-intensive, which adversely affects the extendability of dataset. Toaddress this problem, we propose to construct rendered harmonization datasetwith fewer human efforts to augment the existing real-world dataset. Toleverage both real-world images and rendered images, we propose a cross-domainharmonization network to bridge the domain gap between two domains. Moreover,we also employ well-designed style classifiers and losses to facilitatecross-domain knowledge transfer. Extensive experiments demonstrate thepotential of using rendered images for image harmonization and theeffectiveness of our proposed network.

Deep Image Harmonization by Bridging the Reality Gap