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

Deep learning models for semantic segmentation rely on expensive,large-scale, manually annotated datasets. Labelling is a tedious process thatcan take hours per image. Automatically annotating video sequences bypropagating sparsely labeled frames through time is a more scalablealternative. In this work, we propose a novel label propagation method, termedWarp-Refine Propagation, that combines semantic cues with geometric cues toefficiently auto-label videos. Our method learns to refine geometrically-warpedlabels and infuse them with learned semantic priors in a semi-supervisedsetting by leveraging cycle consistency across time. We quantitatively showthat our method improves label-propagation by a noteworthy margin of 13.1 mIoUon the ApolloScape dataset. Furthermore, by training with the auto-labelledframes, we achieve competitive results on three semantic-segmentationbenchmarks, improving the state-of-the-art by a large margin of 1.8 and 3.61mIoU on NYU-V2 and KITTI, while matching the current best results onCityscapes.

Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency