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

Semantic image segmentation is an essential component of modern autonomousdriving systems, as an accurate understanding of the surrounding scene iscrucial to navigation and action planning. Current state-of-the-art approachesin semantic image segmentation rely on pre-trained networks that were initiallydeveloped for classifying images as a whole. While these networks exhibitoutstanding recognition performance (i.e., what is visible?), they lacklocalization accuracy (i.e., where precisely is something located?). Therefore,additional processing steps have to be performed in order to obtainpixel-accurate segmentation masks at the full image resolution. To alleviatethis problem we propose a novel ResNet-like architecture that exhibits stronglocalization and recognition performance. We combine multi-scale context withpixel-level accuracy by using two processing streams within our network: Onestream carries information at the full image resolution, enabling preciseadherence to segment boundaries. The other stream undergoes a sequence ofpooling operations to obtain robust features for recognition. The two streamsare coupled at the full image resolution using residuals. Without additionalprocessing steps and without pre-training, our approach achieves anintersection-over-union score of 71.8% on the Cityscapes dataset.

Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes