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

Autonomous driving is becoming a reality, yet vehicles still need to rely oncomplex sensor fusion to understand the scene they act in. The ability todiscern static environment and dynamic entities provides a comprehension of theroad layout that poses constraints to the reasoning process about movingobjects. We pursue this through a GAN-based semantic segmentation inpaintingmodel to remove all dynamic objects from the scene and focus on understandingits static components such as streets, sidewalks and buildings. We evaluatethis task on the Cityscapes dataset and on a novel synthetically generateddataset obtained with the CARLA simulator and specifically designed toquantitatively evaluate semantic segmentation inpaintings. We compare ourmethods with a variety of baselines working both in the RGB and segmentationdomains.

Semantic Road Layout Understanding by Generative Adversarial Inpainting