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

Fully convolutional models for dense prediction have proven successful for awide range of visual tasks. Such models perform well in a supervised setting,but performance can be surprisingly poor under domain shifts that appear mildto a human observer. For example, training on one city and testing on anotherin a different geographic region and/or weather condition may result insignificantly degraded performance due to pixel-level distribution shift. Inthis paper, we introduce the first domain adaptive semantic segmentationmethod, proposing an unsupervised adversarial approach to pixel predictionproblems. Our method consists of both global and category specific adaptationtechniques. Global domain alignment is performed using a novel semanticsegmentation network with fully convolutional domain adversarial learning. Thisinitially adapted space then enables category specific adaptation through ageneralization of constrained weak learning, with explicit transfer of thespatial layout from the source to the target domains. Our approach outperformsbaselines across different settings on multiple large-scale datasets, includingadapting across various real city environments, different syntheticsub-domains, from simulated to real environments, and on a novel large-scaledash-cam dataset.

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation