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

Training models dedicated to semantic segmentation requires a large amount ofpixel-wise annotated data. Due to their costly nature, these annotations mightnot be available for the task at hand. To alleviate this problem, unsuperviseddomain adaptation approaches aim at aligning the feature distributions betweenthe labeled source and the unlabeled target data. While these strategies leadto noticeable improvements, their effectiveness remains limited. To guide thedomain adaptation task more efficiently, previous works attempted to includehuman interactions in this process under the form of sparse single-pixelannotations in the target data. In this work, we propose a new domainadaptation framework for semantic segmentation with annotated points via activeselection. First, we conduct an unsupervised domain adaptation of the model;from this adaptation, we use an entropy-based uncertainty measurement fortarget points selection. Finally, to minimize the domain gap, we propose adomain adaptation framework utilizing these target points annotated by humanannotators. Experimental results on benchmark datasets show the effectivenessof our methods against existing unsupervised domain adaptation approaches. Thepropose pipeline is generic and can be included as an extra module to existingdomain adaptation strategies.

Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection